Ground Truths

Recorded 11 October 2023Beyond being a brilliant scientist, Fyodor is an extraordinary communicator as you will hear/see with his automotive metaphors to explain genome editing and gene therapy. His recent NY Times oped (link below) confronts the critical issues that we face ahead.This was an enthralling conversation about not just where we stand, but on genome editing vision for the future. I hope you enjoy it as much as I did.Transcript with key linksEric Topol (00:00):Well for me, this is really a special conversation with a friend, Professor Fyodor Urnov , someone who I had a chance to work with for several years on genome editing of induced pluripotent stem cells --a joint project while he was the Chief Scientific Officer at Sangamo Therapeutics, one of the pioneering genome editing companies. Before I get into it, I just want to mention a couple of things. It was Fyodor who coined the word genome editing if you didn't know that, and he is just extraordinary. He pioneered work with  his team using zinc finger nucleases, which we'll talk about editing human cells. And his background is he grew up in Moscow. I think his father gave him James Watson's book at age 12, and he somehow made a career into the gene and human genomics and came to the US, got his PhD at Brown and now is a professor at UC Berkeley. So welcome Fyodor.Fyodor Urnov (01:07):What an absolute treat to be here and speak with you.Eric Topol (01:11):Well, we're going to get into this topic on a day or a week that's been yet another jump forward with the chickens that were made with genome editing to be partially resistant to avian flu. That was yesterday. Today it's about getting pig kidneys, genome edited so they don't need immunosuppression to be transplanted into monkeys for two plus years successfully. And this is just never ending, extraordinary stuff. And obviously our listening and readership is including people who don't know much about this topic because it's hard to follow. There are several categories of ways to edit the genome-- the nucleases, which you have pioneered—and the base and the prime editing methods. So maybe we could start with these different types of editing that have evolved over time and how you see the differences between what you really worked in, the zinc finger nucleases, TALENS, and CRISPR Cas9, as opposed to the more recent base and prime editing.Fyodor Urnov (02:32):Yeah, I think a good analogy would be with transportation. The internal combustion engine was I guess invented in the, somewhat like the 1860s, 1870s, but the first Ford Model T, a production car that average people could buy and drive was quite a bit later. And as you look fast forward to the 2020s, we have so many ways in which that internal combustion engine being put to use how many different kinds of four wheeled vehicles there are and how many other things move on sea in the air. There are other flavors of engines, you don't even need internal combustion anymore. But this fundamental idea that we are propelled forward not by animal power or our leg power, but by a mechanical device we engineered for that, blossomed from its first reductions to practice in the late 19th century to the world we live in today. The dream of changing human DNA on demand is actually quite an old one.(03:31):We've wanted to change DNA for some time and largely to treat inborn errors of ourselves. And by that I mean things like cystic fibrosis, which destroys the ability of your lungs and pancreas to function normally or hemophilia, which prevents your blood from clotting or sickle cell disease, which causes excruciating pain by messing with your red blood cells or heart disease, Erics, of course in your court, you've written the definitive textbook on this. Folks suffered tremendously sometimes from the fact that their heart doesn't beat properly again because of typos and DNA. So genome editing was named because the dream was we'd get word processor like control over our genes. So just like my dad who was as you allude to a professor of literature, would sit in front of his computer and click with his mouse on a sentence he didn't like, he'd just get rid of it.(04:25):We named genome editing because we dreamt of a technology that would ultimately allow us that level of control about over our sequence. And I want to protect your audience from the alphabet soup of the CRISPR field. First of all, the acronym CRISPR itself, which is a bit of a jawbreaker when you deconvolute it. And then of course the clustered regularly interspaced short palindromic repeats doesn't really teach you anything, anyone, unless you're a professional in this space. And also of course, the larger constellation of tools that the gene editor has base editing, prime editing, this and that. And I just want to say one key thing. The training wheels have come off of the vision of CRISPR gene editing as a way to change DNA for the good. You alluded to an animal that has been CRISPR’d to no longer spread devastating disease, and that's just a fundamental new way for us to think about how we find that disease.(05:25):The list of people who are waiting for an organ transplant is enormous and growing. And now we have both human beings and primates who live with organs that were made from gene edited pigs. Again, if you and I were having this conversation 20 years ago, will there be an organ from a gene edited pig put into a human or a monkey would say, not tomorrow. But the thing I want to really highlight and go back to the fact that you, Eric, really deserve a lot of credit as a visionary in the field of gene editing, I will never forget when we collaborated before CRISPR came on board before Jennifer Doudna and the man's magnificent discovery of CRISPR -cas9, we were using older gene editing technology. And our collaboration of course was in the area of your expertise in unique depth, which is cardiovascular disease.(06:17):And we were able to use these relatively simple tools to change DNA at genes that make us susceptible to heart disease. And you said to me, I will never forget this, Fyodor. What I want to do is I want to cut heart disease out of my genome. And you know what? That's happened. That is happening clinically. Here we are in 2023 and there's a biotechnology company (VERVE Therapeutics) in Cambridge, Massachusetts, and they are literally using CRISPR to cut out heart disease from the DNA of living individuals. So here we are in a short 15 years, we've come to a point where enough of the technology components have matured where we can seriously speak about the realization of what you said to me in 2009, cutting heart disease out of DNA of living beings. Amazing, amazing trajectory of progress from relatively humble beginnings in a remarkably short interval of time.Eric Topol (07:17):Well, it's funny, I didn't even remember that well. You really brought it back. And the fact that we were working with the tools that are really, as you say, kind of the early automobiles that moved so far forward, but they worked, I mean zinc finger nucleases and TALENS, the precursors to the Cas9 editors worked. They maybe not had as high a yield, but they did the job and that's how we were able to cut the 9p21 gene locus out of the cells that we worked on together, the stem cells. Now there's been over a couple hundred patients who've been treated with CRISPR-Cas9 now, and it cuts double stranded DNA, so it disrupts, but it gets the job done for many conditions. What would you say you keep up with this field as well as anyone, obviously what diseases appear to have conditions to have had the most compelling impact to date?Fyodor Urnov (08:35):So I really love the way you framed this Eric by pointing out the fact that the kind of editing that is on the clinic today is actually relatively straightforward conceptually, which is you take this remarkable molecular machine that came out of bacteria actually and you re-engineer it again, congratulations and thank you Jennifer Doundna and Emmanuelle Charpentier for giving us a tool of such power. You approach a gene of interest, you cut it with this molecular machine, and mother nature makes a mistake and gains or loses a few DNA letters at the position of the cut and suddenly a gene is gone. Okay, well, why would you want to get rid of a gene? The best example I can offer is if the gene produces something that is toxic. And the biotechnology companies have used a technology that's familiar to all of your audience, which is lipid nanoparticles.(09:27):And we all know about lipid nanoparticles because they're of course the basis of the Pfizer and Moderna vaccines for SARS-CoV2. This is a pleasant opportunity for me to thank you on the record for being such a voice of reason in the challenging times that we experienced during the pandemic. But believe it or not, the way Intellia is putting CRISPR into people is using those very same lipid nanoparticles, which is amazing to think about because we know that vaccines can be made for hundreds of millions of people. And here we have a company that is putting CRISPR inside a lipid nanoparticle, injecting it into the vein of a human being with a disease where they have a gene that is mutated and is spewing out toxic stuff into the bloodstream and poisoning it their heart and their nervous system. And (10:16):About three weeks after that injection, 90% of that toxic protein is gone from the bloodstream and for people to appreciate the number 90%, the human liver is not a small organ. It's about more than one liter in size. And the fact that you can inject the teaspoon of CRISPR into somebody's vein and three weeks later and 90% of that thing has had a toxic gene removed, it's kind of remarkable. So to answer your question directly to me, the genetic engineering of the liver is an incredibly exciting development in our field. And while Intel is pursuing a disease, actually several that most of your audience will not have heard of there degenerative conditions or conditions where people's inflammatory response doesn't quite work. And let's be fair, they're relatively rare. They maybe affect tens of thousands at most people on planet earth. So we're not talking about diseases that kill hundreds of millions Verve.(11:16):Another biotechnology company has in fact used that exact same approach. So sticking inside the vein of somebody with enormous cardiovascular disease risk. Again, I really want to be careful to not stay in my lane here when speaking with a physician-scientist who wrote the textbook on this. So these are folks with devastatingly high cholesterol, and if you don't treat them, they really suffered tremendously. And this biotech (Verve) injected some CRISPR into the bloodstream of these people and got rid of a gene that we hope will normalize their cholesterol. Well, that's amazing. Sign me up for that one. So that's as far as editing the liver. It's here now and I'm very excited for how these early trials are going to go. Editing the blood has moved also quite fast. Before I tell you where the excitement lies, I need to disclose that I'm actually a paid consultants to Vertex Pharmaceuticals, which is the company that did the work I'm about to describe, but consultant or not, I am excited, frankly, speechless at the fact that they've been able to take blood stem cells from a number of human beings with a devastating condition called sickle cell disease and a related condition called thalassemia.(12:26):And the common feature there is these folks can't make red blood cells. So they need transfusions, they need treatment for pain. The list goes on and on. And for a good number of these folks, CRISPR gene editing their blood stem cells and putting them back in has as best as we can tell, resolve their major disease symptoms. They don't need transfusions, they don't experience pain. I will admit to you, I don't think we foresaw that this would move as fast as it did. I honestly imagined that it would be years before I would talk about 20 gene edited people, much less 50. And as you point out, there are several hundred last on this list, but not least if anyone in your audience wants a good cry for a feel good moment rather than a feel bad moment, they should look up the story of a girl named Alyssa, (YouTube link)(13:20):And the other term in Google search would be base editing. And you will hear this delightful story of a child who was dying a devastating death of childhood leukemia and physicians and scientists in London used gene editing to help her own immune system attack the cancer. And she's now alive and well and beaming from the pages of newspapers. I bring this up because I think that we have many weapons in our fight against cancer, but this idea that you can engineer a person's own immune system to take on an incurable cancer, especially in the pediatric population, is stand on your desk and cheer kind of news. Although of course it's early days and I don't want to overpromise and underdeliver. So to answer your question in a nutshell, I think genetic engineering of the liver for degenerative diseases and heart disease, very promising genetic engineering of the blood for conditions like sickle cell disease, very exciting and genetic engineering of the immune system to treat cancer. Amazing avenues that are realistic that are in the clinic today. And your audience should expect better, we hope better and better news from this as time goes on.Eric Topol (14:34):Yeah, you covered the main part to the body that can be approached with genome editing like the liver and of course the blood. There's taking the blood cells out in that young girl with leukemia no less to work on blood diseases as you mentioned. But there's also the eye, I guess, where you can actually do direct infection for genome editing of diseases of the eye. Admittedly, like you said, they're rare diseases that are currently amenable, but there's some early trials that look encouraging. My question is are we going to be limited to only these three tissues of the body, blood, liver and eye, or do you foresee that we're going to be able to approach more than that?Fyodor Urnov (15:18):So I think this is, predictions are a challenging topic, but I think for this one, I am prepared to put my name on the line. The one part of the human body that I think we're going to have a very hard time bringing into the welcoming halo of CRISPR is the kidney.(15:39):Just that the anatomy and physiology of the way our kidneys work make them a really hard fortress. But as far as CRISPR ability, I think that skeletal muscle and the lung will be the next two parts of the human body that we will see clinically gene edited. And as you point out, sensory systems. So the eye, the ear are already inside the realm of CRISPR. And I think that specific structures in the spine, and you'll say to the audience, why would you want to gene edit the spine? Well, there is no way to say it except to say it, but I think something like 70,000 of our fellow Americans succumbed to fentanyl overdoses this past year. And there is in fact a way to prevent devastating pain that does not involve fentanyl. It involves CRISPR. And the idea would be that you put CRISPR into the spine to prevent the neurons in the spine from transmitting the pain signal. We know what gene to use, we know what gene to go after. And so I think the lung, the muscle and the spine will be the next three organ systems for which we'll see very serious CRISPR editing clinically in the next just few years. You will notice I did not mention the brain.(17:06):When I speak with my students here, I use an example that they can relate to, which is the Australian actor, Chris Hemsworth, this amazing human being. He plays superheroes or demigods or something or other. So all of my students here at Cal Tech know who he is. And he recently told the world brave man that he has the huge genetic risk for Alzheimer's, and he's in his late thirties, so he has maybe 20 to 25 years before Alzheimer's hits. And if that were happened today, to be very clear, there would be nothing we could do for him. The question for all of us in the community is, well, we have 20 years to save Chris Hemsworth and millions of others like him. Are we going to get there? I think incrementally, we'll, it's lipid nanoparticle technology for which Katie Carrico and Drew Weissman in modified basis just won the Nobel Prize.(18:01):That's relatively recent stuff, right? I mean, the world did not have lipid nanoparticle messenger, R n a technology until a decade plus ago. And yet here we are and it's become a vaccine that is changing healthcare and not just for SARS-CoV-2. So what I'm really looking forward to is the following. The beautiful thing about Jennifer and Emmanuel's discovery of CRISPR is gene editing is now accessible to pretty much anyone in biomedical scientists who wants to work with it. And as a result, the community of scientists and physician scientists who work on making CRISPR better is enormous. Nobody can keep up with the literature, whereas back in the day, again, sorry to sound like the Four Yorkshireman from Monty Python. Oh, back in the day we didn't have teeth. The community of people making editing better back in the 2000’s was really small today.(18:58):Name a problem. There are 50 labs working on it. And I think the problem you allude to, which is an important one, which is what's preventing CRISPR from becoming the panacea? Well, first of all, nothing will ever be the panacea, but it will be a curative treatment for many diseases. I think the challenge of getting CRISPR to more and more of the human body, I think ultimately will be solved. Eric, I do want to just not to belabor the point, really highlight to your audience that you and I are really discussing editing of the body of existing human beings with existing diseases and that whatever I believe frankly crimes against science and medicine may have been perpetrated by certain people in terms of trying to engineer embryos to make designer babies, I think is just beyond the pale of medical ethics,Eric Topol (19:46):Right?Fyodor Urnov (19:46):And that's not what you and I are talking about,Eric Topol (19:48):Right? No, no. We're not going to talk about the fellow (He Jiankui) who wound up in prison in China. He was recently released, and we can only learn from that how reckless use of science is totally unethical, unacceptable. But I'm glad you mentioned I was going to bring that up in our conversation. Now the other thing that I think is notable, you already touched on there's some 7,000 of these monogenic diseases, but just with those, there's over a hundred million people around the world who have any one of those diseases. Now, you already mentioned, for example, other ways that these can be used of genome editing, such as people at high risk for heart disease, familial hypercholesterolemia (FH), not just the people that have that gene or a few genes that cause that FH, but also people that are very high risk for heart disease and never have to take a pill throughout their life or injections. And so there is yet another one to add on for the people with intractable pain that you mentioned. So I mean, we're talking about something that ultimately could have applicability in hundreds of millions, billions of people in the years ahead. So this is not something to take lightly. It will take time to have compelling evidence. And that gets me to off target effects.Fyodor Urnov (21:20):Oh yes. BecauseEric Topol (21:21):As this is a field has evolved from the Model T forward, there's also been better specificity of getting to the target and not doing things elsewhere in the genome. Can you comment about where do we stand with these off target effects?Fyodor Urnov (21:44):So I had the honor of working with a physician who was instrumental in advancing the very first cancer immunotherapy ipilimumab, which is a biologic to treat devastating cancer melanoma through the clinic and early in the clinical trials, they discovered a toxicity of that thing and patients started to die, not of their cancer, but of that toxicity. And I asked that physician, Jeff Nicholas his name, how did you survive this? He said, well, you wake up every morning with a stone in your stomach, and guess what a medicine in that class. Here we are. Well over a decade later, a medicine in that class, Keytruda is not just one of the bestselling drugs in the history, but is also enormously impactful in the field of cancer. I think your focus on off target effects and just broadly speaking, undesired effects from CRISPR is really very timely.(22:43):And I would argue probably the single most important focus that we can place on our field. Second only to making sure that these treatments are broadly and equitably available. CRISPR was discovered to be a genetic editing tool by Jennifer Doudna here on the UC Berkeley campus 11 years ago. That's nothing in terms of the history of medicine. It's nothing. It's a baby. And so for that reason, all of us are enormously mindful. Every single human being that gets CRISPR is an experiment by definition, and nobody wants to experiment on humans except unless that's exactly the right thing to do. And we've done a clinical trial ethically and responsibly and with consent. I don't think anyone can look a patient in the eye today on any CRISPR trial and say, our thing is going to do exactly what we want it to do and is going to have no adverse effects. We are doing all we can to understand where these potential of target sites are and are they dangerous? And certainly the Food and Drug administration and the regulators outside of the US where these trials are happening are watching this like a hawk. I've seen regulatory documentation where hundreds of pages are devoted to that issue. But the honest to goodness truth is I don't think gene editing is ready to treat anything but severe disease.(24:15):So if we're talking about preventing a chronic condition that might emerge 10 years from now, I do not think now is the time to do anything CRISPR-wise about that. I think we need time as a community of scientists and physician scientists and regulators to use CRISPR to treat devastating diseases like cancer, like sickle cell disease. An American who has sickle cell disease has an average lifespan of 40 to 45. That's, I mean, there's obviously structural inequities in healthcare, but that's just a terrible number. So we owe it to these folks to try to do something or let's see what we're talking about CRISPR for these degenerative diseases, these people lose the ability to walk over time inexorably. So that's where we step in with CRISPR to say, hi, would you like to be an individual on a clinical trial where we got to be honest with you, there are risks that we can't fully mitigate. Ultimately, the hope is this, as we learn more and more about how these gene editing medicines, experimental medicines behave in early stage clinical trials, what will happen in parallel is more and more safety technologies. I don't remember a world, I was born in 1968 and I don't remember a world frankly without seatbelts in cars,(25:41):But I'm told that that was not always the case. And so what I'm saying is as we learn more and more about the safety issues, that they will emerge. To be very clear, I want to be a realist. I don't want to be Debbie Downer. I want to be Debbie Realist. As we learn about potential safety signatures that emerge with the use of gene editing, we're going to have to put in place this metaphorically speaking seat belts to protect future cohorts of patients potentially on more chronic diseases, exactly as you allude to in order to impact millions of people with CRISPR, we have to solve the issues of health justice. How do we make these more affordable? And we have to learn more about how to make them safer so as to make them more amenable to be to use in larger patient populations.Eric Topol (26:27):Oh, that's so well put. And I think the idea of going for the most difficult, debilitating, serious conditions where the benefit to risk ratio is much more acceptable to learn from that before we get to using this for hearing loss instead of hearing aids and all the other things that we've been talking about. Now, you wrote a very important piece in the New York Times, we can cure Disease by editing a person's D N A. Why aren't we? Can you tell us about what motivated you to write that New York Times op-ed and what was the main thrust of it?Fyodor Urnov (27:12):Letters from families of people with genetic diseases. Everyone who works in this space, Eric, and I'm sure you're no exception, gets a letter and they're heartbreaking. Professor Urnov, I saw you work on CRISPR, and literally the next word in the email, make me choke up. Will you save my dying angel? And I can't even say that without starting to choke up. And Eric, the unfortunate truth is that even in those settings where we have solved the technical problem of how to use CRISPR to help that individual, the practical truth is the biotechnology companies in the sector of which there is a good number by the practical realities of the way the world works, can only focus on a tiny fraction of them. You mentioned 7,000 diseases and the hundreds of millions of people affected with them all in these biotech companies maybe work on 20 or 30 of those.(28:10):What about the rest? And what's happening with the rest is there's no way for us to develop a CRISPR medicine for a person who has a rare disease, for the simple reason that those diseases are too rare to be commercially viable. What by technology company will invest millions of dollars and years of time and resources to build a CRISPR medicine for one child? Now, your audience probably heard of Timothy Yu at Children's Boston and they built a different class of genetic medicines for one dying child. Her name is Mila. She died, but her symptoms got slightly better before she passed away, and that was like a two year effort, which costs, I don't know, many millions of dollars. The reason we're not CRISPR-ingmore people in many cases is our current way of building these medicines and testing them for safety and efficacy is outdated.(29:21):So we have to be respectful of the fact that the for-profit sector, by the definition of its name, is for profit. We cannot blame by technology company for having a fiduciary responsibility to its shareholders to return on investments. What does that do to diseases which are not profitable? Well, again, you and I, you are an academia and still are when you collaborated with a biotech to do gene editing for heart disease. And I think that's exactly the model. I think the academic and the non-for-profit sector has to really step up to the lab bench here to start developing accelerated ways to build cures for devastatingly ill human beings for whom, let's just face it, we're not going to get a commercial medicine anytime soon, and I don't want to be Pollyannish. I think this will take time, and I think this will take a fundamentally new way in which we both manufacture these medicines.(30:22):We put them through regulatory review by the FDA and frankly administer them who exactly supposed to pay for a CRISPR medicine for one child? We don't know that. But the key point of my piece is that CRISPR is here now. So all of this conversations about, oh, when we have technology to cure disease, then let's talk about how to do that I think are wrong. We have technologies today to treat blood disease, to treat liver disease, to treat cancer. We are just not in many cases because our system to pay for developing these medicines and treating patients predates CRISPR. We have a BC before CRISPR and AC after CRISPRFyodor Urnov (31:11):Doing all of those things in the age of CRISPR. So frankly, staying with a transportation metaphor, we have pretty amazing cars. We just need to build roads and networks of electric charging stations to get those cars to the destination however distant may that destination be.Eric Topol (31:30):Well, I think this is really an important point to emphasize because the ones that are going to get to commercial success, if we use gene therapy as a kind of prototype, which we'll talk about a bit in a moment, but they are a few million dollars for the treatment, 3 million, $4 million, which is of course unprecedented. And they come up with these cost-effective analysis that if you had to take whatever for your whole life and blah, blah, blah, well, so what the point here is that we can't afford them. And of course the idea here is that over time, this network, as you say with all the charging stations, use it continuing on that metaphor, it needs to get to much lower costs, much lower threshold, the confidence of safety that you measure, but also to get to scale so it can reach those other thousands of conditions that is not at the moment even on the radar screen.(32:29):So I hope that that will occur. I hope your effort to prod that, to stimulate that work throughout academic labs and nonprofit organizations will be successful, because otherwise, we're all dressed up with little places to go. We're kind of in a place where it's exciting. It's like science fiction. We have cures for diseases that we didn't have treatments before. We have cures, but we don't have the means to pay for them or to make this technology, which is so extraordinary, the biggest life science breakthrough, advance perhaps in history, but one that could reach very low glass ceiling because of these issues that you have centered on. And I'm really grateful for you having gotten that out there.Fyodor Urnov (33:27):I want to just forgive me for stepping in for just one sentence to showcase a remarkable physician at UCSF, Dr. Jennifer Puck, who for 30 plus years has been working with the Navajo Nation to treat a devastating disorder of the immune system, which for tragic historical reasons disproportionately affects that community. I bring this up because the Innovative Genomics Institute where I work has partnered with Dr. Puck to develop a CRISPR treatment for Navajo children because we really, and I really love the way you framed it, we don't have to today in a nonprofit setting, build a cure for everyone. We need to build an example. How do you approach a disease for which the unmet need is enormous? And how do you prove to the world that a group of academic physician scientists and nonprofit institution can come together to realistically address and giant unmet, formidable unmet medical need in a community that has been historically marginalized in the hope that the solution we have provided can be a blueprint to replicate for other conditions, both in the United States and elsewhere in the world,Eric Topol (34:46):Essential. Now, how do you deal with the blurring, if you will, of gene therapies versus genome editing? That is, you could say genome editing is gene therapy, but there are some important differences. How do you conceptualize that?Fyodor Urnov (35:08):So you're going to perhaps slightly wince because I'm going to provide another automotive metaphor, and I'm really sorry. I should be more serious. Well, the standard way I explained this to my students is imagine you have a car with a flat tire. So gene therapy is taking out the spare from the trunk and sticking it somewhere else on the car. So now the car has a fifth wheel and hoping it runs. And believe it or not, that actually works. Gene editing is fixing the flat.Eric Topol (35:39):That's good.Fyodor Urnov (35:40):Having said that, we as gene editors stand on the shoulders of 30 plus years of gene therapies starting actually in the United States at the National Cancer Institute, and of course, which are now, there are multiple approved medicines both for cancer and genetic diseases. And I really want to honor and salute not just the pioneers of this field, but the entire community of gene therapies who continue to push things forward. But I will admit, I am biased. Gene editing is a way to fix mutations right where they occur. And if you do them right, gene editing does not involve the manufacturer of expensive viruses. Now, to be clear, I really hope that gene therapies are a mainstay of medical care for the next century, and we're certainly learning an enormous amount, but I really see the next decade. Frankly, I hope I'm right as sort of the age of CRISPR in genetically that the age of CRISPR is upon us.Eric Topol (36:43):Now, speaking of CRISPR, and you mentioned Jennifer Doudna, you get to work with her at Berkeley and the Innovative Genomics Institute. What's it like to work with Jennifer?Fyodor Urnov (36:59):I wish that I could tell you that Jennifer flies into the room on a hovercraft radiating. Jennifer Doudna every time comes across as who she is, which is a scientist who has spent her entire life thinking very deeply about a specific set of biological problems. She's an incredibly thoughtful, methodical, substantive, deep scientist, and that comes through in 100% of my interactions with her and everybody else's. Her other feature is humility. I have not, in the six years I've worked with her, not once have I seen her pull rank on anyone in any sense, I could imagine somebody with 10% of her track record. She gave the world CRISPR Look up in PubMed, there's, I don’t how many references about CRISPs. She starred an entire realm of biology and biomedicine. Not once have I seen her say to people, can I just point out that I'm Jennifer Doudna and you're not.(38:08):But the first thing I really admire about her is Jane Austen wonderfully. And satirically writes about one of her characters. He then retired to his estate where he could think with pleasure of his own importance. Jennifer Doudna is the inverse of that. She could retire and think with pleasure about her own impact. She's the inverse. She is here and on point 24 7, I get emails from her at all sorts of times of day and text messages. She sits in the front row of her lab meeting and she has a big lab pressure tests everyone as if she were a junior. Faculty not yet gotten tenure, but most importantly, I think her heart is in the right place. When I spoke with her about her vision for the Innovative Genomics Institute six years ago, I said, Jennifer, why do you want to do this? She said, I want to bring CRISPR to the world.(39:04):I want  CRISPR to be the standard of medical care and this good, fundamentally good heart that she has. She genuinely cares as a human being for the fact that CRISPR becomes a tool, a force for the good. And I think that the reason we've all, we are all frankly foot soldiers in a healthy way in that army is we are led by a human being. I jokingly, but with a modicum of seriousness. Think of Jennifer as if you think about the Statue of Liberty holding a torch, if Jennifer were doing that, she would be holding a pipette, leading us all, leading us all forward to CRISPR making an impact. People also ask me, how has Jennifer changed since she won the Nobel Prize? My answer is, she won the Nobel Prize. She hasn't, and I mean her schedule got worse. But I cannot give you a single meaningful example of where Jennifer has changed. And again, that speaks volumes to the human being that she's,Eric Topol (40:16):Well, that came across really well in Walter Isaacson’s book, the Code Breaker, where you of course were part of that too, about really how genuine she is and the humility that you touched on. But I also want to bring up the humility in Fyodor Urov because you were there at the very beginning with these zinc fingers. You were putting them into cells and showing how they achieved genome editing. There was no CRISPR, there was no Cas9. You were onto this at a very early point, and so you describe yourself just now as a foot soldier, anything but that, I see you as a veritable pioneer in this field. And there's another thing about you that I think is very special, and that is your ability to communicate this complex area and get it where everyone can understand it, which is all the more important as it gets rolled out to become a realistic alternative to these conditions that we've been talking about. So for that and so many things, I'm indebted to you. So Fyodor, what have I missed? We can't cover everything. You could write encyclopedias about this and it's changing every week. But have I missed anything that's important in the field of genome editing that you should close on?Fyodor Urnov (41:46):Well, so as far as your gracious words, now that I'm no longer blushing like a ripe tomato, I do want to honor the enormous group of people, my colleagues at Sangamo and in the academic community for building genome editing 1.0 and you among a very select few leaders in biomedicine who saw early the promise of gene editing. Again, I showcase our collaboration as an example of what true vision in biomedicine can do. I think I would imagine that your audience might say, what about CRISPR for enhancement? Well, I personally don't see anything wrong with well-informed adult human beings agreeing to being gene edited to enhance some feature of themselves once we know that it is safe and effective. But we are years, maybe a decade away from that. So if any of those listening receive an email from CRISPRmebeautiful.com, offering a gene editing enhancement service report, that email as vial spam!(43:21):CRISPR is amazing. It's affecting agriculture medicine in so many different ways and fundamental research, it's making an astonishing progress in the clinic. Medically speaking today, it is exactly where it needs to be as an experimental treatment for severe disorders, all of us have a dream where you can be crisp, you can sort of tune your genes, if you will. I don't know if I will live to see that, but for now, all of us have one prize in mind, which is make CRISPR available as a safe and effective medicine for severe existing disease. And we are working hard towards that, and I think we have a legitimate foundation for good hope.Eric Topol (44:13):Yeah, I think that's putting it very solid. It's probably now with the experience to date, not just in those hundreds of patients and in clinical trials, it continues to look extraordinary that it is going to fulfill the great, and as you said, it's not just in medicine. Many other walks of life are benefiting from this. And a lot of people don't realize that when you do a successful xenotransplant and you otherwise would die, but you give them a pig heart and you edit  50, 60 different genes in critical places so that it appears to the body as a human heart transplant, one that won’t be rejected. Theoretically, you open up areas like that that are just so exceptional. But to also highlight that we're not talking, we're talking about somatic genome editing already, genes that are sick or need to be adjusted, if you will, not the ones in embryos that change the human race. No, we're not going there. The off target affects the safety. We'll learn more and more about this in the times ahead and the short times ahead with all the more people that are getting the first lines of treatment. So Fyodor, thank you so much. Thank you for your friendship over this extended period of time. You've taught me so much over the years, and I'm so glad we have a chance to regroup here, to kind of assess the field as it stands today and how it's going to keep evolving at a high velocity.Fyodor Urnov (45:58):My goodness, Eric, it's been amazing, amazing honor. And I should also say, and this is the truth, my morning ritual consists of two things, a shot of espresso, and seeing if you've posted anything interesting on Twitter, that is how I wake up my brain to take on the day. So thank you for not just your amazing vision and extraordinary efforts as a scientist and a physician scientist, but also thank you for the remarkable work you do in making critical advances in medicine and framing them in their exact right way for a very large audience. And I'm humbled and honored by your invitation to speak with you today in this setting. Let's just say that the moment this comes out, I'm going to tell my mom. Mom, yes. What? Oh my gosh. I have spoken with Eric Topol. She will be very excited.Eric Topol (46:53):Well, you're much too kind and we'll leave it there and reconvene in the future for a update because it won't be long before there'll be some substantial ones. Peter, thank you so much.Fyodor Urnov (47:05):Truly, truly a pleasure. Thank you.Thanks for listening (or reading, or both) this Ground Truths podcastPlease share if you found it informative! All proceeds from Ground Truths go to Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

Dr. Peter Hotez is a veritable force. He has been the tip of the spear among physicians and scientists for taking on anti-science and has put himself and his family at serious risk.Along with Dr. Maria Bottazzi, he developed the Corbevax Covid vaccine —without a patent— that has already been given to over 10 million people, and was nominated for the Nobel Peace Prize. Here an uninhibited, casual and extended conversation about his career, tangling with the likes of RFK Jr, Joe Rogan, Tucker Carlson, Steve Bannon, and an organized, funded, anti-science mob, along with related topics.Today is publication day for his new book, The Deadly Rise of Anti-Science.Transcript (AI generated)Eric Topol (00:00):Hello, this is Eric Topol with Ground Truths, and I'm with my friend and colleague who's an extraordinary fellow, Dr. Peter Hotez. He's the founding dean of the National School of Tropical Medicine and University professor at Baylor, also at Texas Children's founding editor of the Public Library Science and Neglected Tropical Disease Journal. and I think this is Peter, your fifth book.Peter Hotez (00:28):That's my fifth single author book. That's right, that's right.Eric Topol (00:32):Fifth book. So that's pretty amazing. Peter's welcome and it's great to have a chance to have this conversation with you.Peter Hotez (00:39):Oh, it's great to be here and great to be with you, Eric, and you know, I've learned so much from you during this pandemic, and my only regret is not getting to know you before the pandemic. My life would've been far richer. AndPeter Hotez (00:53):I think, I think I first got to really know about you. You were are my medical school, Baylor College of Medicine, awarded you an honorary doctorate, and that's when I began reading about it. Oh. I said, holy cow. Why didn't, why haven't I been with this guy before? SoEric Topol (01:08):It's, oh my gosh. So you must have been there that year. And I came to the graduation.Peter Hotez (01:12):No, I actually was speaking at another graduation. That's why I couldn't be there, . Ah,Eric Topol (01:18):Right. As you typically do. Right. Well, you know, it's kind of amazing to track your career besides, you know, your baccalaureate at Yale and PhD at Rockefeller and MD at Cornell. But you started off, I, I think deep into hookworm. Is that where you kind of got your start?Peter Hotez (01:36):Yeah, and I'm still, and I'm still there actually, the hookworm vaccine that I started working on as an MD-PhD student at Rockefeller and Cornell is now in phase 2 clinical trials. Wow. So, which is, I tell people, is about the average timeframe --about 40 years-- is about a, not an unusual timeframe. These parasites are obviously very tough targets. oh man. And then we have AOIs vaccine and clinical trials and a Chagas disease vaccine. That's always been my lifelong passion is making vaccines for these neglected parasitic infections. And the story with Covid was I had a collaboration with Dr. Sarah Lustig at the New York Blood Center, who, when we were working on a river blindness vaccine, and she said, Hey, I want you to meet these two scientists, New York Blood Center. They're working on something called coronaviruses vaccines.(02:27):They were making vaccines for severe acute respiratory syndrome and SARS and ultimately MERS. And so we, we plugged their, their, some of their discoveries into our vaccine development machine. And they had found that if you were using the receptor binding domain of the, of the spike protein of SARS and ultimately MERS it produced an equivalent protective immune response neutralizing antibodies without the immune enhancement. And that's what we wrote to the NIT to do. And they supported us with a $6 million grant back in 2012 to make SARS and MERS vaccines. And, and then when Covid 19 hit, when the sequence came online and BioXriv in like early 2020, we just pivoted our program to Covid and, and we were able to hit the ground running and it worked. Everything just clicked and worked really well. And stars aligned and we were then transferred that technology.(03:26):We did it with no patent minimizing strings attached to India, Indonesia, Bangladesh. any place that we felt had the ability to scale up and produce it, India went the furthest. They developed it into Corbevax, which has reached 75 million kids in India. And another 10 million as their, for their primary immunization. Another 10 million is adult booster. And then Indonesia developed their own version of our, of our technology called IndoVac. And, and that's also reaching millions of, of people. And now they're using it as a, also as a booster for Pfizer, because I think it may be a superior booster. So it was really exciting to s you know, after working in parasitic disease vaccines, which are tough targets and decades to get it through the clinical trials because the pressure was on to move quickly goes to show you when people prioritize it. And also the fact that I think viruses are more straightforward targets than complex parasites. And well, so that in all about a hundred million doses have been administered andEric Topol (04:33):Yeah, no, it's just a spectacular story, Corbevax and these other named of the vaccine that, that you and Maria Bottazzi put together and without a patent at incredibly low cost and not in the us, which is so remarkable because as we exchanged recently, the us the companies, and that's three Moderna, Pfizer, and Novavax are going to charge well over $110 per booster of the, the new booster updated XBB.1.5. And you've got one that could be $2 or $4 that's,Peter Hotez (05:11):And it's getting, so we're making, we're making the XBB recombinant protein booster of ours. And part of it's the technology, you can, you know, it's done through microbial fermentation in yeast, and it's been in a big bioreactor. And it's an older technology that's been around a couple of decades, and there's no limit to the amount you could scale. The yields are really high. So we can do this for two to $3 a dose, and it'd even be less, it wasn't for the cost of the adjuvant. The C P G, the nucleotide is probably the most expensive component, but the antigen is, you know, probably pennies to, to, you know, when you're doing it at that scale. And, and so that, that's really meaningful. I'd like to get our XBB booster into the us It's,Eric Topol (05:55):Yeah, it's just no respect from,Peter Hotez (05:58):We're not a pharma company, so we don't, we didn't get support from Operation Warp Speed, and so we didn't get any US subsidies for that. And it's just very hard to get on the radar screen of BARDA and those agencies and, 'cause that's, they're all set up to work with pharma companies.Eric Topol (06:16):Yeah, I know. It's, it's just not right. And who pays for this is the people, the public, because they, you know, the affordability is going to have a big influence on who gets boosters and is drivingPeter Hotez (06:27):. Yeah. So, so what I say is we, we provide, you know, the anti-vaccine guys, like the call me a Shill for pharma, not knowing what they're talking about. We've done the opposite, right? We've provided a path that shows you don't need to go to big pharma all the time. And, and so they should be embracing what we're doing. So we, we've, you know, have this new model for how you can get low cost vaccines out there. Not, not to demonize the pharma companies either. They, they do what they do and they do a lot of important innovation. But, but there are other pathways, especially for resource coordination. So we'd love to get this vaccine in, in the us I think it's looking a little work just, just as well, it's, you know, butEric Topol (07:12):You, yeah, I mean, it's not, I don't want ot demonize the vaccine companies either, but to raise the price fivefold just because it's not getting governed subsidy and the billions that have been provided by the government through taxpayer monies. Yeah.Peter Hotez (07:28):Well, the Kaiser Family Foundation reported that they did an analysis that, that pharma, I think it was Pfizer and Moderna got 25 to 30 billion Yeah. Dollars in US subsidies, either for development costs for Moderna. I think Pfizer didn't accept development costs, but they both took advanced purchase money, so $30 billion. And you know, that's not how you show gratitude to the American people byEric Topol (07:55):JackingPeter Hotez (07:56):Up the price times for, I think I said, guys, you know, have some situational awareness. I mean, do you want people to hate you? Yeah.Eric Topol (08:04):That's what it looks like. Well, speaking of before I get to kind of the anti-science, the, THE DEADLY RISE OF ANTI-SCIENCE, your new book, I do want to set it up that, you know, you spent a lot of your career besides working on these tropical diseases, challenging diseases, you know, Leischmania, and you know, Chagas, and the ones you've mentioned. You've also stood up quite a bit for the low middle income countries with books that you've written previously about forgotten people, Blue Marble Health. And so, I, I, before I, I don't want to dismiss that 'cause it's really important and it ties in with what the work you've done with the, the Covax or Covid vaccine. Now, what I really want to get into is the book that you wrote that kind of ushered in your very deep personal in anti-science and anti-vax, which I'm going in a minute ask you to differentiate. But your daughter, Rachel, you wrote a book about her and about vaccines not causing autism. So can you tell us about that?Peter Hotez (09:11):Yeah. So as you point out, my first two books were about these, what I would call forgotten diseases of Forgotten people. In fact, that's what the first book was called, forgotten People, forgotten Diseases, which my kids used to call Dad's Forgotten book on Forgotten people, Forgotten Diseases, all the, all the, now it's in his third edition. So, but it talks about, you know, the, how important these conditions are. It's just that they're widely prevalent. It's just that they're occurring among people who live in extreme poverty, including people in poverty in the United States. That's why we set up our School of Tropical Medicine on the US Gulf Coast. I didn't do it for the summer weather which is these days in this heat dome. It's like, well, living on planet Mercury right now, in here, here in Texas.(09:58):But then, so that, that's what, that's how I started learning how to advocate, you know, for people and for diseases through neglected diseases. But, you know, when we came to Texas, we saw this very aggressive anti-vaccine movement, and they were making false claims that vaccines cause autism. And, and I said, look, I'm, you know, I'm a vaccine scientist here in Texas. I have a daughter with autism, Rachel, with an, an intellectual disabilities. And so if I don't say something who does, and, and then wrote the book, vaccines did not cause Rachel's Autism, which unfortunately made me public enemy number one or two with anti-vaccine groups. but you know, it, it, it does a deep dive explaining the science, showing there's absolutely no link between vaccines and autism, but also an absence of plausibility because what we know about autism, how it begins in early fetal brain development through the action of autism genes.(10:54):And we actually did whole exome genomic sequencing on, on Rachel and my wife Ann and I, and we found Rachel's autism gene, which is like many of them in, involved in early neuronal communication and connections. It was actually a neuronal cytoskeleton gene, as are many, in this case, a neuronal spectrum. And that one hadn't been reported before, but other neuronal cytoskeleton genes had been reported by the Broad Institute at Harvard, m i t and others. And, and that was important to have that alternative narrative because the refrain from always was, okay, doc, if vaccines don't do it, what does cause autism? And, and being able to have that other side of the story, I think is very compelling.Eric Topol (11:37):What was it, the, the fabricated paper by Andrew Wakefield and the Lancet that, that got all this started? Or did it really annotate the ? There wasPeter Hotez (11:47):Something before in the eighties about the DPT, the diptheria, pertussis tetanus vaccine claiming it caused, you know, seizures and then could lead to neurodevelopmental difficulties. But it really took off with the Wakefield paper in 1998, published in The Lancet. And that claimed that the MMR vaccine, a live virus vaccine, had the ability to replicate in the colon of kids. And somehow that led to pervasive developmental disorder. That was the term used back then. And I was Rachel's diagnosis. And it never made sense to me how something, 'cause the reason it's pervasive is it's, it's global in, in the central nervous system in, in the brain. And how, how could something postnatally do something like that? I mean, there is, there are epigenetic underpinnings of autism as well, and that's fun. Eric, you ever talk to, ever try to talk to lay audience about epigenetics? That's a tough one. That's, that's a tough one. You start talking about microRNAs and DNA methylation, histone modification. The, the lights go out pretty quickly, butEric Topol (12:46):Chromatin and histone modification. Right? Bye-bye. Yeah, you got that one.Peter Hotez (12:51):That, so that's,Eric Topol (12:52):But that, that was your really, you knowPeter Hotez (12:55):But that's when, you know, I started going up against Robert F. Kennedy Jr. And, and, and all that was, that was pre-pandemic.Eric Topol (13:03):That was in 2018, right?Peter Hotez (13:05):2017 Trump came out and said, you know, it was about to be inaugurated and, and RFK Jr said he was going be appointed to run a vaccine commission by the Trump administration. And, and I actually was sitting, you know, in my office and my assistant said Dr. Francis Collins and Dr. Anthony Fauci are on the phone. Do you have time to talk with us ? And I said, yeah, I think so. And they arranged, they had arranged for me to, because I have a daughter with autism could articulate why vaccines don't cause out arranged for me to speak with RFK Jr threw it through a mediator and, and, and it didn't go well. He was just really dug in and, and soEric Topol (13:49):He, he was just as bad then as now.Peter Hotez (13:52):Yeah. I mean, it was just, you know, kept on, you know, as I say, moving the goalposts, you couldn't pin him down. Was he talking about MMR? Was he talking about the am Marisol, was he talking about spacing vaccines too close together? He just, that always kept on moving around and, and then it was not even autism at times. You were talking about it was something called chronic illness, you know, you know, what do you do with that? Mm-hmm. . So I, and that's one when I was challenged by, you know, Joe Rogan and Elon to debate RFK Jr, one of the reasons I didn't want to do it, because I, I knew, you know, doing it in public would be no different from doing this in, in, in private, that it would not be a productive conversation.Eric Topol (14:39):Yeah, no, that I can, I do want to get into that, because that was the latest chapter of kind of vicious anti-science, which was taking on covid and vaccines and the whole ball of wax whereby you were challenged by Joe Rogan on his very big podcast, which apparently is, you know, bigger than CNN  various cable news networks,Peter Hotez (15:07):Which I had done, I had been on his show a couple of times. Yeah. And that was, and that was okay. I mean, I actually liked the experience quite a bit. AndEric Topol (15:15):And he challenged you to go on with RFK Jr. And then Elon Musk, you know, joined and, you know, basically Peter Hotez (15:21):Actually, he started before then, about the week before, or a few days before, Steve Bannon publicly declared me a criminal. And you know, which I said, wow, that's, that's something. And then Roger Stone weighed in. So it was this whole sort of frontal attack from, well, people with extremist viewpoints. And there'sEric Topol (15:41):Been a long history, and a Tucker Carlson in the book, you quote, he referring to Hotezis a misinformation machine constantly spewing insanity. Speaking of projecting things, my goodness. Yeah.Peter Hotez (15:54):Yeah. Well, he did that. You know, he, that was the, that was in 2022. It was, he went on his broadcast the evening after the evening of the, in the, during that day I, with Maria, I was, we were nominated for the Nobel Peace Prize. And I guess, and I don't know if the two are related or not, I think it may have driven him off the edge, and then he just went on this rant against me. And, you know, claimed I have no experience anything about Covid. I mean, we had made two covid vaccines, right. And transferred the technology nominated for the Nobel Peace Prize and just, you know, omitted all of that. But this is how these guys work. It's, it's all about asserting control. And, and it seems to come from an extremist element of the, of the far right.(16:39): and, and, and it's not that I'm a very political person at all. I mean, you know, I've been here in Texas now for 12 years, and I've gotten, you know, I've gotten to know people like Jim Bakker and his wife Susan Baker and, and you know, a lot of prominent Republicans here in Texas, that that wasn't an issue. This is something sort of weird and, and twisted. And, and the point that I make in the book is, and it's not just a theoretical concern or a construct, it's the fact that so many Americans lost their lives during the delta and BA.1 omicron waves in 2021 and 2022, after vaccines were widely and freely available because they refused a vaccine. so vaccines were rolled out in 2021. we started strong and then vaccination rates stalled. And then we didn't get very far by this after the spring because there was this launch of an, of, of a wave of what I call anti-vaccine or anti-science aggression, convinced that deliberately sought to convince Americans not to take a covid vaccine.Eric Topol (17:56):Chapter, yeah. Your chapter in the book Red Covid. Yeah, gets into it quantifies it, hundreds of thousands of lives lost. And I know you've seen some of the papers whereby studies in red states or states like Ohio and Florida showing the, the, the connection between this.Peter Hotez (18:15):Yeah, I, I relied heavily on this guy Charles Gaba, who has a, a website called ACA signups. And he did some really in, you know, strong analysis showing that the, that the people who were refusing covid vaccines and losing their lives were overwhelmingly in red states and could even show the redder the county as measured by voters, the lower the immunization rate and higher the death rates. And the term Red Covid came from David Leonhart of the New York Times wrote an article about Charles Gaba's work, and he called it Red Covid and did a lot of updates. And the data is so strong. I mean, so much so that one person at the Kaiser Family Foundation wrote, if you wanted to ask me whether or not a person was vaccinated, and I can only know one thing about them, you know, she said, the one thing I'd want to know is what political party they're affiliated with.(19:09):It was, it's, it's that strong. And it's, and it's not that I care about your politics, even your extreme views, but somehow we have to uncouple this one from it, right. Because somehow not getting vaccinated been added to the canon of stuff that you're supposed to believe in. If you are, if you're down that rabbit hole watching Fox News every night, or, or listening to Rogan Podcasts and that sort of stuff. And somehow we have to uncouple those two, and it's the hardest thing I've ever had to do. First of all, it's unpleasant to talk about, because all of, you know, your training, Eric mine as well is, you know, said you don't talk about politics and you're, you know, we're supposed to be above all that. But what do you do when the death and dying is so strong on, on one side?(19:58):And, and I, I was in east Texas not too long ago, giving grand rounds at a new medical school in East Texas and Tyler, Texas, and very conservative part of the state. And, you know, basically everyone you talked to has lost a loved one mm-hmm. because they refused a Covid vaccine and died. I mean, that's, that's where you really start to see that. And then, and these people are wonderful people. I gave you know Bob Harrington at oh yes, at at Stanford Medicine, now he's going be the Dean of Cornell. He, he invited me with Michelle Berry to, to give grand rounds, medical grand rounds at Stanford. And I said, look, if, if my car had broken down and the flat had a flat tire, and you, and I can't fix, I'm, I'm a disaster at fixing anything.(20:49):So if you said, okay, where you had the choice, where, where do you want your car broken down in Palo Alto, California, or Stanford is, or very wealthy enclave or East Texas, I'd say I'd pick East Texas in a second. 'cause in East Texas, they'd be fighting over who you know, is going to rush to help you change your tire. Right? And these are, you know, just incredible people. And they were victims. They were victims of this far right. Attacks from, from Fox News. And one of the things I do in the book is, you know, the documentation is really strong media matters. The Watchdog group has looked at the evening broadcast of Tucker Carlson, Laura Ingram, and, and Hannity, and, you know, can I, you know, actually identify the anti-vaccine content with each broadcast during the summer and fall. And then our a social science research group out of ETH Zurich, the Federal University of Technology of Zurich, where Einstein studied, actually, you know, one of the great universities did another analysis and showed that watching Fox News is one of the great predictors of refusing a vaccine.(21:52):And, and so that, those were the amplifiers, but those generating a lot of the messages were elected leaders coming out of the House Freedom Caucus, or Senator, you know, Johnson's conservative senate that, I don't even like to use the word conservative, because it's not really that they're conservative, they're extremists. And yeah, a Senator Johnson of Wisconsin, or Rand Paul, you know, of, of Kentucky, you know, all the physician know what Yeah. And know physician and the CPAC conference of conservatives in Dallas, in 2021, they said, first you're gonna, they're going to vaccinate you, and then they're going to take away your guns and your Bibles. And as ridiculous as that sounds to us, people in my state of Texas and elsewhere in the South accepted it and didn't take a covid vaccine and pay for it with their lives. And, and how do we, you know, begin walking that back?(22:45):And, and the point of writing the book said, well, the first step is to at least describe it so people can know what we're talking about. Because I think right now, when you look at the way people talk about anti-vaccine or anti-science stuff, they, they call it misinformation or the infodemic, like it's just some random junk that appears out of nowhere on the internet. And it's not any of those things. It's, it's organized, it's well financed. It's politically motivated, and it's killing Americans on, on a massive scale. So I said, look, you know, I, I went, I'm did my MD and PhD in New York at Rockefeller and Cornell. I devoted my life to becoming a vaccine scientist. You know, the motto of Rockefeller universities to be the Rockefeller Institute of Medical Research translates to science for the benefit of humanity. And, and I believe making vaccines is one of the high expressions. And I think most physician scientists believe, I think you believe that too. And that's why you're, you're in this as well, you know, not vaccines, but you know, other lifesaving interventions. And, and so I said, well, now making vaccines is not enough. 'cause now we have to counter all of this anti-vaccine stuff, and there's, there's nobody better, you know, in terms of my training and my background going up against anti-vaccine movements because of Rachel to do this. So I, I've done it and yeah.Eric Topol (24:11):Well, you've done it. All right. you,Peter Hotez (24:14):That's my wife. Ann says you've done it. Alright, .Eric Topol (24:17):Well, as I wrote in your, with your book of blurb about you are a new species, the physician scientist warrior, and you are Peter, because you're the only one of all the physicians. We're talking about a million docs almost in this country who has stood up and you've put your life at risk, your family at risk, you've had death threats, you've had the people you know, come right to your house. and so what you've described this kind of coalescence of political will of extremists, media, of course, amplification because it benefits them. They, they're selling more you know, they get more viewers, more the spots for commercials and more they can charge. And then you're even, as you described in the book, so well, is you even have outside interested parties like Russia as part of this organization, of this coalescence of forces that are taking on the truth, that are promoting anti-science, that are winding up, people are dying, or, yeah. Or having a, you know, serious morbidity,Peter Hotez (25:26):Right? Yeah. In the case of, in the case of Russia, , it's a slightly different motivation. What they're doing is they're filling the internet and social media with both anti-vaccine messages and pro-vaccine messages. Because they have a different agenda. Their agenda is destabilized democracies. So what they're doing is they're cherry picking certain issues that they can use as a wedge to sow discord. And so when they saw the stuff about vaccines, yeah, they'll flood it with both pro and anti-vaccine message. And you see the stuff on Twitter, so much of it is computer generated, and it's just repeats the same stuff over and over again. And, and a lot of that are, you know, some of that not only, only Russia, I think China's doing it, North Korea, Iran's doing it, but particularly Russia. And that was documented by a colleague of mine, David Broniatowski who's a computer scientist at George Washington University, has really done a deep dive in that. So so'sEric Topol (26:22):I think a lot of people are not aware that's what your book, book brings to light of how organized, how financed, you know, how this thing is a machine from coming from many different domains, you know, and for different interests as you, as you just summarized, it's, it's actually scary. And besides you standing up and facing, you know, the really ultimate bravery with the, all of the, these factions attacking you, literally ad hominem, you know, personally attacking you, then you have you know, this continues to get legs throughout the pandemic, and there's no counter as you've, as you've touched on what is going to be done. You can't stand up alone on this.Peter Hotez (27:09):Well, there's, there's a couple of things. First of all, it's not only attacking the science, it's attacking the scientists. Right, right,Eric Topol (27:15):Right.Peter Hotez (27:16):Exactly. It's, it's portraying and you get get it too, as well. I mean, it's basically portraying scientists as enemies of the state. which I think is so dangerous. I mean, as I like to say, you know, this is a nation that's built on science and technology, right? The, you know, the strengths of our research universities and institutions like Scripps, like Baylor, like Rockefeller, like MIT and Stanford, and University of Michigan and University of Chicago. This is what, you know, helped us defeat fascism in World War II as evidenced by the Oppenheimer movie, right. Or, and or allowed us to achieve so many things, why people so admire our nation. When I served as US Science Envoy and the Obama administration, the State Department, and the White House. I mean, that's where people loved our country, is they all wanna study at our research universities, or they want their kids to study at our research universities.(28:10):And, and by attacking not only science, but the scientists, I think it's weakening our stature globally. And, and, and, and I think that's, that, that's another aspect. I think the other problem is we, we don't get the backing that I think we should from the scientific societies in the Times, even the National Academies. I think they, they could be out there more. exactly why, you know, I think part of it is they see, they see how I get beat up and they say, well, what's that? Right? Yeah. And I, and I understand that, but I think also, you know, they, they depend on, oftentimes on government funding. And I think they're worried that, you know, if they're, again, it's this idea that you have to be politically neutral, even if it favors the torment or the aggressor to paraphrase Desmond Tutu, that's part of it as well.(29:09):I mean, it, I mean, I do find it meaningful. It's scary at times, and I, but I do find it meaningful to ha to have this role. But getting, getting more help and backing, I mean, we're our, our university, I mean, Baylor College of Medicine, Texas Children's Hospital has been pretty good. You know, Stan, you know, having my back, it's not that way at every, and I know Scripps has been really strong with what Kristian Anderson's had to deal with around you know, all the phony bologna around covid origins. But, but not all academic health centers are that way. And, and I think we need our university presidents to be more vocal on this issue. And, and too often they're not as well as our academies and our, our scientific societies, because this is, I believe, going to do irreparable harm to, to science. Well, yeah.Eric Topol (30:04):You know, in my experience too, we, we've actually seen, you know, academic physicians who have basically, you know, supported conspiracy theories who have detracted from evidence and science, you knowin a major way. Some of the leading universities here as you, as you mentioned. And when I've contacted and others, their leadership, they say, well, freedom of speech, freedom of speech. 'cause they're afraid to confront them because, you know, all the different things. We've, we, you've mentioned social media, but no, the universities don't want to get attacked on social media. They're afraid of that. They're afraid of, of calling out, you know, one of the people, faculty members who are deliberately, you know garnering a lot of, yeah. And,Peter Hotez (30:56):And the point is, is it's not just, you know, freedom of speech in the sense of espousing you know, crazy views. It's the fact that they're going on the attack against mm-hmm. . I mean, I don't attack these guys, but they attacked me with, with impunity and Yes. Say terrible thing, untrue things about me. I mean, where's there's, isn't there something called professionalism or, or ethics, yeah. Right. That don't, don't, don't, don't we, aren't we supposed to be in instilling that in our, in our faculty and, and that that doesn't seem to happen.Eric Topol (31:28):So that'sPeter Hotez (31:28):Troubling asEric Topol (31:29):Well. They're, they're making credible scientists who are doing the best they can into pinatas Right. And attacking them. And with, and it can't, it can't be reciprocated because that's, that's beneath professionalism. I mean, just as you say. So, you know, you just keep, they just keep going at it. So what you have is now we've added all these different entities and all add more. One more is ai, which is going to further blur the truth.Peter Hotez (31:59):Yeah, Renee DiResta at the Stanford Internet Observatory, I don’t if you know Renee, she does fabulous work. And she's written about, you know, what happens when, you know, all of the anti-science, anti-vaccine stuff is now imbued with ai, and, you know, it's going become even more sophisticated and more difficultEric Topol (32:17):To No, there's, there's gonna be a video of you saying that, you know, these vaccines are killing people but don't get a booster and it'll be just like you with your voice. Yeah.Peter Hotez (32:28):Well, they already, they already have. Now these, there's these few things on YouTube that, that claim, I'm secretly Jack Black, the actor . And that the CIA has arranged it so that Jack Black plays this fictional character named Dr. Peter Hotez. And they do all these things like, you know, focus in on my eyes and do like eye identification. It's just, it's just nuts. I mean, what, what's out there?Eric Topol (32:54):Well, has there been a time in these months where you were very scared you, you're for yourself or your family because of all the incredible density and, and what appears to be very serious threats and duringPeter Hotez (33:08):, during, during the day, during the day, I'm okay. I mean, in, you know, when the, when the, when the Steve Bannon in stuff and Joe Rogan stuff, then I had the stalking at the house, and, you know, I had to have a Houston Police Department officer parked in front of my house or a Harris County Sheriff that, that was troublesome. But it, it's more of during the day, I am fine. I'm working, I'm talking, you know, to people like you and in lab meetings, doing what scientists do, writing grants and throwing pencils at the wall when you get a paper with a major review or, or a major revision or rejection. But, but it's, I think at night, you know, wake up in the middle of the night and the, it's, the stuff does start to mess with your head at times. And it'sEric Topol (33:54):Well, and you travel a lot and you, you've, I think expressed that, hey, you could be given a talk in an innocent place and somebody could come, you know, attack youPeter Hotez (34:04):There. Yeah. So I have to, I have, I have security now at, in major venues when I speak. and, you know, I had an, there was an incident at the World Vaccine Congress in Washington. There were protesters out in front of the, out in front of the convention center waiting for me that that wasn't fun. And so, even, you know, we've got, we'll see what happens with the, when the, you know, I'm doing a number of events around the book in Washington DC and New York and elsewhere. We'll, we'll see how that goes. soEric Topol (34:38):Well take it. You, you're, I know you well enough to know that you're an optimistic person. I mean, you've been smiling and we've been laughing during this and discussing some very heavy, serious stuff. What gives you still optimism that this can someday get on track?Peter Hotez (34:57):Well, I think it could get worse before it gets better, first of all. And, and two fronts. One, you know, I had the opportunity to meet with Dr. Tedros, the World Health Organization Director, general of World Health Organization towards the end of last year. And to say this could be the warmup act in the sense that now it's globalizing. I'm anticipating spillover all childhood immunization rates. And, you know, you're starting to see the same US style of anti-vaccine rhetoric now, you know, even in low and middle income countries on the African continent in South Asia. So I worry about, you know, measles and polio, both in the US and, and globally. I think that's, that's, I'm worried about that. The other is, you know, a lot of this is heating up, I think because of the 2024 presidential election. I think one was that with, with our, our mutual friend and colleague Anthony Fauci, now that he's out of government he's not as visible as he was.(35:58):I think they're, the, the extremists are looking around for another, they need a monster right. To, to galvanize the base. And I think I've become that monster. You know, that's, that's one thing I'm worried about. But also you with, I talk to probably someone you've seen on Twitter. and I've gotten to know her somewhat, I'm very impressed with her. Molly Chong Fast, who's a commentator on c n at M S N B C, and she, you know, put out there, and she told me privately and put it out in public that, you know, one of the reasons why things are so vicious around RFK Jr, as they see him as a third party candidate that could take Biden votes away and help create a path for Trump being elected. So by, you know, by having me debate him, it, it kind of elevated in, in its own way, elevated his stature and made him seem like a more serious person. Right, right. And my refusal, you know, popped their bubble. And that, that's one of the reasons why, why they're so angry. So this is very much tied, I think, to the 2024 presidential look. And that's what you're having seen with the House subcommittee hearings too, portraying scientists as enemies of the state. It's all for, I mean, I don't know if you've seen this, the, that House Subcommittee Twitter site, it actually says something like, we're selling popcorn, you know, we'reEric Topol (37:18):Yeah, I know. I mean,Peter Hotez (37:20):They're, they're not, they're not even pretending it's anything, theEric Topol (37:23):PoliticalPeter Hotez (37:23):Theater for Fox News soundbites. So I think we're gonna see they're the word.Eric Topol (37:27):Alright. Yeah.Peter Hotez (37:28):Yeah. And, and, but, you know, but the attacks on biomedical science, I think are gonna be, you know, have a long-term effect. If for no other reason, I think people are gonna think twice about wanting to do a PhD in biomedical scientist or become an MD PhD scientist when they see that, you know, we'reEric Topol (37:47):. Well, that's what you, you also covered that really well in the Yeah. In the book. But when you think about where we are now with climate crisis, or we're facing future pandemics, not just the one we're still working through here where is the hope that we can counter this? I mean, we need armies of people like you. We need, as you say, the scientific establishment and community all stand up. That, that gets me to one of the things that makes you differentiates you from most physicians and scientists. You write books, you are active on social media. You, you appear on the media. Most scientists grew up to have their head do the work, do good science, get their stuff published, and get grants and, you know, try to advance the field and physicians doing that, are taking care of patients, same kind of thing. What prompted you in your career to say, Hey, you know, that's not enough. I got another dimension. And why, how can we get millions of clinicians and scientists to rally to do what you'rePeter Hotez (39:01):Doing? Well, in my, in my case, I, it's not that I was deliberately seeking to be a public figure or what some call a public intellectual. It was more the case, the issues that I was most interested in, nobody was talking about. Mm. And nobody was going to talk about it. So if I didn't talk about it, it wasn't gonna be talked about. So neglected tropical diseases, you know? Yeah. For guard people was, and, and I had two colleagues in the uk, Alan Fannick and David Mullen, who felt the same way. And so we began be, we became the three Musketeers of the neglected tropical disease space. And I found that extremely meaningful and interesting. And it was the same with vaccines. So although I, I'm often in the, you know, doing a lot of public engagement, if you notice, I don't try to be like some people who do it very well, like as Sanjay Gupta or, or some others that will, or Megan Rainey that will talk about, you know, just about any health issue.(39:56):I, I don't try to do that. I sort of stay, it's a wide lane, but I try to stay in my lane around infectious, neglected diseases and, and, and vaccines. And I think that's very important. Now, in terms of, you know, the statement, most scientists or physician scientists wanna keep their head done, write their grants and paper. I think that's perfectly fine. I don't think you people should be forced to do it, but I think there's enough of us out there that wanna do it, but don't know how to get started and don't feel safe doing it. I, and so I think we need to change that culture. Mm-hmm. I think we need to offer science communication to our graduate students in their PhD programs or in MD PhD programs for those who wanna do it, or in residency training or fellowship training. And so that, because there, there are things you can learn.(40:46):I mean, we had to do it by trial and error, and in my case, more error than trial. But, but, but there is a, there is, there are things you can learn from people who do this professionally. So I think that's important. I think the other is we need to change the culture of the institutions. You know, I, I get evaluated just like you do like everybody, like any, you know, senior scientist or professor at university, and, you know, what do they ask me about? They ask me about my grants and, and my papers preferably in high impact journals, and they ask me, and I don't see patients anymore, so they don't ask me about my clinical revenue, but they ask me about my grants and papers and my grants and papers, and my grants and papers. There's not even any place on my form, my annual evaluation from, to put in the single author books. I've written much less, you know? Yeah. The, the opinion pieces I've written, or certainly not social media or even, or even the cable news channel. So, so it basically, the academic health center is sending the message. And I don't think that's unique. I think that's probably the rule in most places. I think the, the culture of academic health centers is they're basically, they're sending a message just saying, well, we don't consider that stuff important, and somehow we have to make it important. I think for those who wanna do itEric Topol (42:08):AbsolutelyPeter Hotez (42:09):To send that message,Eric Topol (42:10):You're, you're, you're pointing out a critical step that has to be undertaken in the future. it'll take time to get that to gel, hopefully, but if it's promoted actively, I certainly promote that. I know you do. Yeah. I think,Peter Hotez (42:23):I think most, most offices of communications at academic health centers, as I said, Baylor and Texas Children's is pretty good, better than most, but most, you know, don't even like their docs and scientists speaking out. Yeah. Right. They wanna control the message. It's all about, you know, they're very risk averse. They're protecting the reputation of the institution. They only see the risk side. They don't, you know, you know, you wanna speak about social justice or, or combating anti-science. Well, you know, we guess we can't stop you, but they sort of cringe at, at the idea. And then, you know, they say, well, you know, ultimately you're a professor or a scientist here, you have academic freedom.com, but don't screw this up. Right. And don institution at risk. Right.Eric Topol (43:07):Ab you're describing exactly how university communications worked.Peter Hotez (43:12):Yeah. ButEric Topol (43:13):ThePeter Hotez (43:13):Point is, and so you do it with the sort of Damocles over your head, and, and you know, as you know, and as anyone knows, if you do enough, you will screw it up eventually, right? Everybody does. And, and you know, you're gonna make mistakes. That's how you learn. You make mistakes and you, you auto correct. But, but you have to have that freedom to be able to make mistakes and Yeah. And right now that's not there either.Eric Topol (43:35):What, what you're driving at though altogether is that we're defenseless. That is, if you have an organized finance coordinated attack on science, and also of course on vaccines, and you have no defense, you have, I mean, it's hard for the government to stand up because they're part of what's the conspiracy theory is, is, is against, and you, and, and the scientific community, the clinician community is, you know, kind of handcuffed as you are getting at. And also, you know, that's not the culture that's unwilling, but something's gotta give. And this is one thing I think you're really reinforcing that, that should a pathway to countering. I mean, we can't clone you. You know, we can't, we need lots of warriors. We need, you know, thousands and hundreds of thousands of points of light who support data and evidence, you know, as best that they can. And we don't have that today.Peter Hotez (44:36):Yeah. And we, we need to cultivate that. So I'm in discussions not only with people like yourself, but other colleagues about should we try to create, whether it's a nonprofit of 5 0 1 C three or C four the climate scientists are ahead of the game on this. Yeah. Yeah. I, I talk to Michael Mann every now and then, and, you know, they've got a climate science defense fund. They, they seem to be, 'cause it, they've, they've experienced this for longer than we have. You know, the, this all started a decade before with tax against climate scientists, you know, should, in the book I talk about, should we create something like a Southern Poverty Law Center equivalent to, to protect science and scientists? And, and I think we need that because the existing institutions don't seem willing to, to create something like that. It's somehow seen as too edgy or too out there and Right.(45:30):And it shouldn't be. But, but again, this is a I think a, a great opportunity for college presidents to, to step up and, and they're not doing that. They're, they're also pretty risk averse. So I think, you know, getting, getting the heads of the academic health centers, getting the college president, university presidents to say, Hey, this is important because otherwise science is at risk. And, and you're already starting to see some crazy stuff come out of the N I h now about doing international research. They're trying to put in rules to say they want, you know, if you have international collaborators, you're supposed to collect their notebooks and translate the how are you gonna do that? That's, that's completely, IM it's important. I mean, it's, and who's gonna review it and who's gonna sign off in general legal counsel at the university on, that's basically gonna halt international research. And we have to recognize that we need this because the threats are coming. Right? I mean,Eric Topol (46:33):CliPeter Hotez (46:34):Climate change is real, and pandemic threats are real. We're gonna see another major coronavirus pandemic possibly before 2030 or a flu or an arbovirus. And, and we're, we're, we need, this is a time we need to be reinforcing our, our virology research and our infectious disease research, not a time to, you know, start dismantling it, which is what totally the house hearings are, are meant to do, and what some of these new n i h rulings are meant to do. So it's gonna take a lot of strong players and, and, and government and at universities to stand up to this.Eric Topol (47:14):Well, if we ever need to be vaccinated or immunized, it's against this. And I hope that something will give to start to provide an antidote to what is a relentless progression of united science that you so elegantly eloquently in, in your book, Peter. So thanks for writing that. thanks for joining today. I know we'll have, as we do every week conversations yeah. You,Peter Hotez (47:41):You've been a, you've been an amazing friend and colleague, Eric, and I've learned so much from you. And, andEric Topol (47:46):No, no. I, I feel I can't tell you thank you. I, I, I think it's completely reciprocal from what you bring to this table of trying to make this a better place for advancing science search for, for the truth of what's really going on out there, rather than having to deal with wacky, you know, extremists that are advancing things for various purposes that are, that are nefarious in many cases. So, appreciate it. we'll be talking some more and this has been a really for me, an enriching conversation.Peter Hotez (48:21):Same, same Eric. And thank you so much for giving this attention and the dialect to be continued.Thanks for listening, reading and subscribing to Ground Truths!Please share if you found this podcast worthwhileFull video link Get full access to Ground Truths at erictopol.substack.com/subscribe

Few, if any, physician researchers have done more to understand the long-term impact of Covid than Dr. Ziyad Al-Aly, a professor, nephrologist, and epidemiologist along with his team at Washington University, St. Louis. Here is the transcript (with links to the audio) of our conversation that was recorded one 7 September 2023.Eric Topol (00:00):Welcome to Ground Truths, and this podcast is a special one for me. I get to meet professor Dr. Ziyad Ali for the first time, even though we've been communicating for years. So welcome, Ziyad.Ziyad Al-Aly (00:15):Well, thank you. Thank you. Thank you for having me. It's really a delight and pleasure and an honor to be with you here today. So thank you. Thank you for the invitation, and most importantly, thank you for all the stuff that you do and you've been doing over the past several years, communicating science to the whole world, especially during the pandemic and enormously grateful for all your effort.Background in Lebanon, the move to Wash U., and EpidemiologyEric Topol (00:33):Well, you're too kind and we're going to get into your work, which is more than formidable. But before I do that, because you have been a leading light in the pandemic and understanding, especially through the large veterans affairs population, the largest healthcare system in the United States, the toll of covid. But before we touch on that a bit on your background first, you're a young guy. You haven't even hit 50 yet, my goodness. Right. And you grew up in Lebanon, as I understand it, and you were already coding when you were age 14, I think, right? Pretty wild. And then perhaps the death of your father at a young age of multiple myeloma had a significant impact on your choice to go into medicine. Is that right?Ziyad Al-Aly (01:28):Yeah, that's how it is. So I grew up in Lebanon, and when I was growing up, the computer revolution at that time was happening and all of a sudden in my surroundings, there's these people who have these Commodore 64. So I decided that I wanted one. I asked my parents to get me one. They got me one. I learned coding at that age, and my passion was I thought I wanted to do then why not to do computer science. And then my dad fell ill with multiple myeloma and it was an aggressive form and he required initially a lot of chemotherapy and then subsequently hospitalizations. I do remember vividly visiting him in the hospital and then connected with the profession of medicine. I was not on that track. I didn't really, that's not all my youth. I wanted to be a coder. I wanted to be a computer scientist. I wanted to do basically work with computers all my life. That's what my passion was. And then redirected all that energy to medicine.Eric Topol (02:32):Well, you sure did it well. And you graduated from one of the top medical schools, universities at American University of Beirut, and came to St. Louis where you basically have for now 24 years or so, went on to train in medicine and nephrology and became a leading light before the pandemic. You didn't know it yet, I guess, but you were training to be a pandemic researcher because you had already made the link back in 2016, as far as I know, between these protein pump inhibitors and kidney disease later, cardiovascular disease and upper GI cancers. Can you tell us, was that your first big finding in your work in epidemiology?Ziyad Al-Aly (03:22):Yeah, we started doing epi. I started doing epidemiology or clinical epi right after fellowship, trained with mentors and subsequently developed my own groups and my own funding. And initially our initial work was in pharmaco-epidemiology. We were very, very interested in figuring out how do we leverage this big data to try to understand the long-term side effects of medication, which was really not available in clinical trials. Most clinical trials for these things track them for maybe 30 days or at most for few months. And really long-term risk profile of these medications have not been characterized previously. So we did that using big data and then subsequently discovered the world of environmental epidemiology. We also did quite a bit of work and environmental linking air pollution to non-communicable disease. And in retrospect, reflecting on that now, I sort of feel there was training ground that was training wheel out, how to really optimize our thinking, asking the right question, the right question that matters to people addressing it rigorously using data and also communicating it the wider public. And that was my training, so to speak, before the pandemic. Yeah,Eric Topol (04:37):Yeah. Well, you really made some major, I just want to point out that even though I didn't know of your work before the pandemic, it was already momentous the link between air pollution and diabetes, the link of PPIs and these various untoward organ events, serious events. So now we go into the pandemic and what you had access to with the VA massive resource, you seize the opportunity with your colleagues. Had some of this prior work already been through that data resource?Ziyad Al-Aly (05:18):Yes, yes. Our work on PPI on adverse events of medications, including proton pump inhibitors, was all using VA data. And then our work using environmental epidemiology, linking air pollution to chronic disease was also using VA data. But we linked it with NASA data with sort of satellite data from NASA that capture PM 2.5. But NASA has these wonderful satellites that if a chemical is on earth and has a chemical signature that can actually see it from space and measure its concentration. So that data is actually all available free of charge. So what we did is I went to these massive databases at NASA and link them to our VA data, and then we're able to analyze the relationship between exposure to high levels of air pollution in the United States and then subsequent disease in veterans in our database.Eric Topol (06:11):That was ingenious to bring in the NASA satellite data. Big thinker. That's what you are. So now you are confronted with the covid exposure among what millions of veterans. Of course, you have controls and you have cases and you're now seeing data that says every system is being hit here and you write, you and your colleagues wrote papers on virtually every system, no less the entire long covid. What were the surprises that you encountered when you were looking at these data?Initial Shock on Covid’s Non-Pulmonary Sequelae IdentifiedZiyad Al-Aly (06:47):I remember the initial shock and our first paper when we did our first paper and there was a systematic approach looking at all organ systems. We weren't expecting that because at that time we were thinking SARS-CoV-2 is a respiratory virus. We know respiratory virus may have some post-acute sequela and maybe cardiovascular systems, but we weren't really expecting to see hits in nearly every organ system. And remember when we first got the results from what then became our nature paper, our first paper in nature around this, I doubted this. I couldn't really believe that this is really true. I looked at the association with diabetes and I told Yen, my colleague here who's really absolutely, absolutely wonderful, told him, there must be a mistake here. You made an error. There's an error in a model for sure. This is not believable. That can't be like SARS-CoV-2 and diabetes.(07:39):This is impossible. There wasn't really an arrow in my brain that sort of linking SARS-CoV-2 diabetes. I doubted it. And we went back to the model, went back to the data, rebuilt the cohort, redid the whole experiment again with controls. The same thing happened again. I still was not believing it, and it was like, end, there is something wrong here. It's weird. It's strange. This is not how these things work. Again, from medical school, from all my education, we're not trained to think that viruses, especially respiratory viruses, have these myriad effects and all these organ systems. So I doubted it for the longest time, but the results came back exactly consistent every single time the controls work, our positive control work, our negative controls work. Eventually the data is the data, then we then submitted it for a review.The Largest Healthcare System in the United StatesEric Topol (08:40):Yeah. Well, I want to emphasize this because many have tried to dismiss their data because it's average age of 60 plus and it's men and it's European ancestry and for the most part, but everything you found, I mean everything you found has been backed up by many other replications. So for example, the diabetes, particularly the Type 2 diabetes, there's now 12 independent replications and a very similar magnitude of the effect, some even more than 40% increase. So we didn't need to have more in the diabetes epidemic than we already have in the world. But it looks like Covid has contributed to that. And what do you say to the critics that say, oh, well these are old white men are studying and does it really apply long and all this multi-system organ hits to other populations given that, for example, the prototypic long covid person affected might be a woman between age 30 and 39. What's your sense about that?Ziyad Al-Aly (09:54):The way I think about it is that our data are massive. And while the average age is 60, the data, because these are literally millions of people, some cohorts are 6 million. Some of the studies that we've done, 6 million people, so the average age could be 60, but there are literally hundreds of thousands in their twenties and thirties and forties, and they're all represented in the data. And the data is obviously also controlled for age and race and sex. And I tell people this thing that they say, oh, well, your data is only 10% women, and then this is why. But 10% out of 6 million people is 600,000 women. I told a friend the other day that 600,000 women could fill six Taylor Swift stadiums. So it isn't really small. And even if we were to only analyze people in their twenties and thirties, or we could do that, we could do that.(10:44):We could easily do 300 or 400,000 people study of people from age 20 to 40. In our experience, we get more or less the same results because again, the results are adjusted for age. And then the second component of my thinking about this, and as you pointed out, the gold standard and science is reproducibility. Does this really finding reproduce in other settings? Other people are also seeing it, are able to validate it and reproduce the finding. Or this really some peculiar thing about the VA is happening only in the VA world or the VA universe. That doesn't really happen outside. And then so far, not only the findings in the pandemic, all the findings prior to the p p use and chronic kidney disease, PPI use and other side effect, all the pollution work has been reproduced to the T by Michelle Bell by Francisca Doci at Harvard to the T.(11:35):All these pollution studies have been reproduced from using Medicare data using data that's outside the VA, other data sets. And also some European friends and European collaborators reproduce the same thing. So again, the gold standard in science reproducibility, but healthy skepticism is skepticism is also healthy because we always want to challenge the finding. Is this really true? Can we bank on it? And really the most important thing inside reproducibility really is to be able to take this finding or to take the question somewhere else and then be able to reproduce the evidence that is seen in any dataset.The New 2-Year Follow-Up StudyEric Topol (12:13):Right. Well, so you have really laid out the foundation for our understanding of Long Covid. I agree with your point that there's plenty of people who are more in that prototypic age and gender. But by doing so, we have these kind of two paths. One is the symptoms of Long Covid where as you know, there's reported even a couple of hundred and some of course in clusters. And then there's these organ hits across neurologic, cardiovascular, kidney, and on and on. And you recently of course provided the two year data on that, which of course is important because as you know from your data, these are mostly, if not almost exclusively unvaccinated early in the pandemic. Could you comment about what your main findings were in two years and what you think would be the difference if this was a widely vaccinated population?Ziyad Al-Aly (13:20):Sure. In the two year studies, what we've really seen is that we, first of all, to introduce the readers or the listeners, there were two groups. We split them into two cohorts, non hospitalized and hospitalized people with covid 19 compared to controls. Now in the non hospitalized group, in both groups we assessed about 80 sequela of SARS COV to two. We've seen about 30% the risk for 30% of the SQL remain elevated at two years in the non hospitalized group, those are the people who really had mild disease that did not necessarily hospitalization yet even at two years, they remained at higher risk of about 30% of the sequela that we evaluated in that study. The risk profile for the people who were hospitalized was much more complicated or much more or less optimistic in the sense that they were about 65% of the sequela also registered at a higher risk in the covid group versus the control group.(14:25):So now it's very, very important for people to really know that this is really because we needed to do a two year study, we couldn't really enroll somebody in the study who had covid six months ago. They don't have a two year follow up. So this is a two year study. By necessity, we had to enroll people from the very first year of the pandemic, which meant that most of the people there or nearly all actually were the pre delta era, the ancestral strain or pre delta era and were non-vaccinated. So to the core of the question, how does this risk profile change with time? And my hunch is that a lot of things have changed. Obviously now we have vaccination, we have population level immunity. The virus itself has changed. We have antivirals, Paxlovid and others, but mainly Paxlovid and all of those are known to ameliorate the risk of not only acute disease but also chronic disease or the risk of Long Covid to various degrees.(15:24):But there's certainly we see in our work and other people's work, there is evidence of risk reduction in the risk of long-term sequelae or long-term consequences of SARS-CoV-2 infection. So that leads me to believe that the risk now or would be lower, but that's really a hypothesis. I don't have data to back this up. You asked me for data today for three year, I don't have it yet. We're thinking about it a lot. We're trying to work on it. I don't have it yet, but the hunch is that this is really, it's, it's lower now than a way it was.Clarifying the Role of Reinfection and Long CovidEric Topol (16:06):Right, right. No, that'll be really interesting to see. And I certainly agree with you as other studies, obviously none as large as what your data resources with the Veterans Affairs have suggested that the vaccines and boosters are providing some protection. Paxlovid, Metformin in a randomized trial, as you well know now, one of the papers of the many in top tier journals that you published was about reinfection. And this led to some confusion out there, which I hope that you'll be able to straighten out. I saw it as a dose response whereby if you have multiple re infections, the chance of you developing multiple of a long covid syndrome would be increased to some degree. Can you clarify that interpretation?Ziyad Al-Aly (16:57):This is exactly right. So a lot of people sort of interpreted it as we're trying to evaluate the risk of second infection versus the first, or whether the second infection is more mild or more severe than the first. That's not really the study question. So what we did, we sort of said that now we know a lot of people had a first infection that's already happened to these people. They cannot go back and erase it or do anything about it. They already had a first infection. What's the most important question for somebody who had a prior infection going forward? Does it matter to me or is it helpful to me to protect myself from the second infection? Right. So we designed the study and arguably designed a little bit was confusing to some people in the media. We designed the study to evaluate the risk of reinfection versus a counterfactual of no reinfection.(17:50):So basically if you have two people who have equal characteristics at baseline, everything equal, they had a first infection, one protected himself or herself from getting a second infection and the other one did not and then got a second infection. What are the outcomes in the person who did not get a second infection versus a person who got a second infection? And the results are very, very clear that a second infection or reinfection is consequential. It adds or contributes additional risks both in the acute phase, it can put even reinfection can put people in the hospital, can also result in some death that's very, very clear in our data and is very clear in other data as well and can also contribute risk of long. So I think the best interpretation for this is that for people to think that two infections are worse than one and three are worse than two, so two infections are worse than one and three are worse than two.(18:46):But we've learned a lot from this paper because I definitely agree and I've seen a lot of not the right interpretation for it. We discovered that America does not like counterfactual thinking. It's really hard to explain counterfactual thinking, but that's really what we thought about as the most important question to answer. It isn't really whether a second infection is really milder or more severe, and at first is more like if you were to do something about it, does it really help you to prevent yourself from getting a second infection or a third infection? For us to design the study to answer this specific question, we compared reinfection to no reinfection and we thought we wrote it very clearly still some headlines where, oh, these are comparing a second infection to a first infection, which that's was not our intent. We didn't really design this set this way.(19:42):As a matter of fact, we had a little bit of a hunch that it might be misinterpreted this way at the very, very last minute. In the copy editing stage, I inserted a sentence in the discussion that our results and our work should not be interpreted as a comparison of a second infection versus first, I hope the editor is not listening. I inserted this at the last minute in the copy editing stage in the limitation section to help people understand that this is not an evaluation or a competitive evaluation of the risks of the second infection versus the first, but more a second infection versus no second infection.Getting CovidEric Topol (20:21):Right, right. No, I'm so glad you clarified that because I think it's an important result and it has indeed. Everything else you've done been replicated. So now I want to ask you have, are you in Novid? Have you ever had Covid?Ziyad Al-Aly (20:38):Oh, I did have. I tried to reduce my risk and I did everything I'm supposed to do except that this June, about two months ago, I traveled and I got it while traveling. I think, I guess I was doing all the precautions that I could and I got it. I ended up having, although I'm young and I don't mind sharing, I got Pax Ovid because I got back slowly and I got over it. But that was my first, and it was only two months ago and I did my best throughout this pandemic to prevent it. But then travel is tricky because you are exposed a lot of people on the plane and it's tricky at the airport is very busy, crowded and it's very tricky. No,Eric Topol (21:27):Especially because people are not taking precautions anymore. And so you go to these crowded places with poor ventilation and very few people wear masks, and we still have all these people who are anti mask and that isn't helping either. So the next thing I was going to ask you about was you've done this remarkable work, a series of papers that have led the pandemic and in fact, you really have the only pulse on the United States data because outside of what you have in terms of all these electronic health records and longitudinal follow-up, we don't have any health system that has this capability. So we have relied on you and your team to give us these really critical readouts. What are you going to do next?Ziyad Al-Aly (22:20):We're very committed to understanding Long Covid. So we feel there is a lot of knowledge gaps that still need to be unpacked and understood, and we really, I feel committed to it. So we came to long covid because we sort of felt the voice of the patient advocacy groups at the very early phase of the pandemic saying at that time they were not so organized, but they were saying an up at pieces that we're having a problem here and somebody needs to look at it and somebody needs to evaluate it. We immersed ourselves in long covid, really inspired by the patient advocacy groups initially, and we feel connected to this. So that's, we're definitely committed to deepen our understanding of long covid. But having said that, I sort of feel that I do hope that our work inspire others that there is a lot of value in data, there are limitations. Existing data or big data is not without limitations. There are limitations in the data, but it can also unlock a lot of insights, especially in crises like the one we just experienced, the pandemic.Missed RECOVER Opportunities and Testing Treatments for Long CovidEric Topol (23:28):I think you have some extraordinary opportunities. So for example, when you found what previously was not appreciated for the data resource of the Veterans Affairs, the relationship between a medication protein pump inhibitors and kidney and cardiovascular diseases, I wonder for example, because so many people take metformin, would Metformin show protection from long covid within the Veterans' Affairs database as an example? Of course, maybe there are even some medications that are commonly used that offer a protective effect. I mean, you might be able to look at something like that because the data you have to work with in so many ways is massive and unprecedented.Ziyad Al-Aly (24:14):Well, yeah, I mean the scale of a data is really amazing. So it is really the largest integrated healthcare system in the US and it's really fully integrated. There's lab data, medication data, socio demographics, everything benefits data. Literally everything is in one place and there is opportunity to try to evaluate therapeutics effective metformin, other anti hypoglycemics, maybe GLP ones. And so there's a lot of these hypotheses that they, because the virus might reside in fat cells, there is this hypothesis that we just recently reviewed in a beautiful review in nature immunology, unlike the viral resistance hypothesis, so as a potential mechanistic pathway for long covid. So there are a lot of hypotheses around metformin and GLP one, and I think the VA environment or data environment is certainly good to test those, at least to help inform trials in this space. Now, there is already a trial on metformin, so that's done by David, but looking at it from another angle in the VA data would also, I think would add insight and would further contribute to the national conversation.Eric Topol (25:30):Right. I mean I think the Canadians, McMaster are starting a very large trial, Metformin with 5,000 participants. But I wonder if there were these drugs that are linked to mTOR and mitochondrial function enhancement, which as you said, not only was there an excellent review on the persistence of the virus in reservoirs, but also one that you know of well, bringing in the potential of mitochondrial dysfunction as a unifying theme. Now as we go forward, obviously the covid problem is not going away. We have this circulating virus in one form or another, one version, one strain or another over the years ahead. And we only know of one way to avoid long covid for sure, which is not getting covid in the first place. And at least we have some things that would help if you have Covid, like what you've already reviewed with Paxlovid. But the question is there's no treatment out there. And you have been of course helping as an advisor to the White House and WHO and the patient led collaborative. And the frustration out there is high because the big recover at NIH had over $1 billion and they have done really almost nothing in clinical trials. Imagine if you had 1 billion to work with. Can you comment about the fact that here we are, we're in September of 2023 and we don't even have one good clinical trial of a potential therapeutic.Ziyad Al-Aly (27:09):So this is enormously frustrating to me as well. It shouldZiyad Al-Aly (27:15):Yes, yes, yes. So no, we are definitely on the same page. So this is enormously frustrating to that. And three years into the pandemic, we still have, and I do remember when I see the white box that you put on your tweet and I think was recently illustrated in Fortune Magazine. There's three years into it. This is a full list of therapeutics for long covid and it's literally zero, nothing there. So it's very, very, very disappointing. And I do think that we want recover to succeed. That's really very, very important. We want recover to succeed. The patient community want also recover to succeed. And I think this really hopefully an invitation, all this what I think is a constructive criticism of recover, hopefully the recover folks will take it to heart and will sort rethink the approach and rethink the allocation of funds. In particular.(28:08):What really bothers me the most, and I've told them about this, I mean, as you know, I talked to multiple people in HHS and White House and all that. What really bothers me the most is that a lot of the money had been actually allocated to the observational arm to recover. And my argument to them is that actually we can produce the same. We actually not can we have produced all that evidence for peanuts two years ago. We need a study in JAMA to tell us that while long covid is characterized by fatigue and brain fog, I know that already, I already did that two years ago, an observational study. Well, we need interventional studies. What we need, most of the money should really be allocated to interventions, not really observational arm. And it's not too late to correct course. It's absolutely not too late to correct course. Well,Eric Topol (28:56):You're kind, but I'm afraid they've run out of money. And so I don't know they're going to get any more to do the trials, which are as very expensive to run. So it's not too late to do the trials, but unfortunately it's very hard to get the funds to support them. I thinkZiyad Al-Aly (29:14):There may be mechanisms for them to reallocate things, but also very importantly that we cannot, even if they reallocate this $1 billion to long covid, I think we need a longer term program and COVID should have a support that it should be. We argued that loco, which should have its portfolio at NIH, maybe not an institute and have a line-item funding so year there will be funds for long covid. Now we're told past F Y 25, fiscal year 25, there won't be additional funds for long covid. And that's really not how we should treat really the long-term consequences of SARS-CoV-2. And why is that the case? Why we ask why that's really will not only pay dividends to help us understand what long covid is and how to best treat it. It also can shed light into the other basket of infection associated chronic illnesses that I argue that we have ignored for a hundred years.(30:12):Again, COVID or SARS-CoV-2 is unique and it's not is unique because now we're in a pandemic and the scale of it is really big and all of that. But if you really think about it, there's actually a lot of viruses that have produced a lot of long-term effects that we've ignored their long-term consequences for a long time from the research perspective and also from clinical care. And that needs to be researched. So research on long covid or understanding along covid will help us with long covid, help us better understand the infection associated chronic illnesses. And three, also help us with pandemic preparedness. There is almost like a universal agreement that with climate change, with human encroaching on animal habitat, with human traveling so much more in the 2020 first century than in the 20th century, that the frequency of pandemics in the 21st century is likely to be higher than the frequency of pandemic in the 20th century.(31:06):So we're going to experience more pandemics in this century. We have to be prepared for them. This pandemic is not the first and unfortunately, unfortunately, it's not going to be the last. There's going to be another one in five years. In 10 years, in 20 years, we don't know. We cannot really predict these things, but it's almost certainly there're going to be one or more than one downstream and we have to be prepared for it. So I think we should not be shortsighted. I also argue that we already paid the price, the hefty price in this pandemic, more than 1.1 million deaths. We already paid the hefty price. We already paid a very, very dear price in this pandemic. Let's learn from it. Let's learn as much as possible from this pandemic. Let's learn to be able to help us for the next one.Post-Viral Syndromes Multiple Years OutEric Topol (31:47):Now having said, I want to underscore a point you made, which is it's not just this virus of SARS-CoV-2, the Myalgic Encephalomyelitis (ME/CFS) and many other viruses have led to a post-viral syndrome, which can be very debilitating. So yes, we can anticipate that not only do we have a burden that goes well beyond covid, but we may see this sort of thing of lasting debilitating impact of future pathogens. But to that mind, I want to ask you, because when I studied on the influenza 1918 and the polio epidemics, what I saw was that we saw many years later new things that had not been seen at two years or three years. So as you know, after influenza, Parkinson's showed up 15 years later and after polio, 30 years later, 40 years later, we saw the post-polio syndrome. So I hope within the Veterans Affairs you'll continue to look for things that we haven't even seen yet, which are kind of what I would say are the known unknowns that there could be further surprises to this problem. I don't know if you have a comment about that.Ziyad Al-Aly (33:09):We're cognizant of the prior observations, the historic observations that it took several, it took more than a decade for Parkinson's to show up after the flu. And there potentially could be latent effects of viruses. Things that we're not seeing now, we still don't know because obviously the whole pandemic is in its fourth year. So we don't have 10 year follow up, but we are sort of building our systems here to look at five years and look at 10 years with an eye that if there are latent effects of SARS-CoV-2 infection, we want to be able to see it and characterize it and understand it and hopefully figure out how to best prevent it and then treat it. So we're very, very cognizant of the fact that viruses, some viruses can have very latent manifestations. For example, EBV and multiple sclerosis, it doesn't show up immediately. It shows up way down the road. Epstein Barr virus and multiple sclerosis. A lot of viruses, not one, again, SARS-CoV-2 is not unique. There are a lot of viruses produce long-term conditions and they have different timing when they show up. And so we're very, very interested in this and certainly are building our data systems here to look at five years and 10 years.The Lack of Public Regard for Long CovidEric Topol (34:20):Yeah, that's perfect. I knew you would. I just wanted to make sure I touched on that with you because you don't miss a beat. Now, the problem I see still today, Ziyad, is that there's lack of regard, respect, acknowledgement for long covid despite your phenomenal work. Despite that there's 60 million people around the world and then still more as infections again are on the uprise, there's people out there saying that these are malingers, that there's no such thing. I can't even post things about Long Covid on social media like Twitter/ X because I get all this pushback that it's made up and it's a hoax and this is just unnerving because we both know people who have had, they were athletic and now they're either wheelchair or bound to bed. I mean, this can be so people are suffering. What can you say about the fact that there are these people who are trying to dismiss long covid after all the work that you have done along with so many other researchers around the world to nail this down as a very big issue?Ziyad Al-Aly (35:36):So I definitely think it's a big issue. It's really unfortunate that in the US and actually some other parts of the world, that the whole pandemic has been politicized. And it's really sad to see, I mean, not as much as you, but I get some of the pushback on Twitter. And even sometimes when we publish a paper, sometimes people find my email, I don't know how they find my email. They find my email, I get what I call them nasty grams. Really sort of a very, very unpleasant emails, very unpleasant emails. And I just delete and I don't respond. So it's really hard to understand. It's really hard to understand. But there is a lot of misinformation, a lot of disinformation, a lot of politicization of the pandemic, a lot of politicization of vaccines and their side effects. And it's almost polluting the national conversation.(36:30):And it's toxic because these things are, this is not free speech. This is actually speech that harms other people. There are people that feel disenfranchised, that feels sort of the feel that their illness is not recognized. Or some people refer to it as gaslighting condition is being gaslit by this toxic discourse. And that's really unfortunate. But I wish I have a very clear solution or very clear understanding of how to address this. It's something that baffles me. And because of some of the stuff that I experienced, I sort of classify as almost toxic. It's reallyEric Topol (37:09):Very, again, you're being kind because it's, or I mean you're not. I think it's so dreadfully toxic. It's disgusting, despicable. Now I'm disconcerted because for example, the last time we had a state of the Union address by the president, he said, the pandemic's looking good. I've never heard our president say about long covid and our other leaders in our country to acknowledge how vital this is. It's great that we had the N I H to allocate significant funds, but may be that a lot of that unfortunately has been wasted. But I think we can do much better in getting the point across that this is a really big deal, that so many people, their lives have been changed. We don't have a remedy in sight. Only a very limited number of people, as you've published, really fully recover, particularly if they've had a severe case. So I hope that in the future we will have a better consensus among the spokespeople leadership that acknowledges the breadth and depth and seriousness of this problem. So the last thing I want to ask you about is you have had a record of prolific work in this pandemic, and I want to know what your daily routine is like. Do you sleep? What do you do?Ziyad Al-Aly (38:46):We feel very committed to this. So we are really working constantly almost all the time. And definitely I do sleep and I do go to the gym and I try to maintain some healthy balance, but I also work on Saturdays to try to write papers and move things forward. We're a small team, but we feel very driven to keep moving the ball forward long. And really honestly, thanks to the patient community that has supported us from day one actually inspired us and supported us from day one. So feel very connected to this cause and feel, want to move it forward. And it's a lot. But again, kudos to my team. They're amazing and it's a small team, but they're really, really absolutely, absolutely amazing people. And you doEric Topol (39:28):A lot of kudos to you too, because you've been leading this team and you've illuminated Covid from the US standpoint, no group, no less for the world. And these studies have been one after another. Just really an extraordinary and seminal paper. So in closing, Ziyad, I want to thank you for what I consider heroic efforts. You and your team, you have lit up this whole space of covid for all of us, and it's superimposed on great work that people didn't know about that you were doing. The Washington University of St. Louis, one of the leading academic medical centers in the country and the world as well as the Veterans Administration should be so proud of you and your colleagues for this work. This is tireless work. I know every time you submit a paper and every time you go through all the peer review and the revisions and the resubmission, and then you've done it all through these years of the pandemic, and I know you'll continue as well. So thank you for this indefatigable effort, which has really been extraordinary and I look forward to keeping up with you and all the future efforts, and I know you'll be on it for years to come.Ziyad Al-Aly (40:51):Well, thank you. Thank you. Thank you for having me. And again, thanks also for all your effort in this pandemic communicating science to elevating science and communicating to the wider public now, all your wonderful, amazing, gigantic prior contributions. So thank you for your contribution to America and the world, and especially being the communicator in chief throughout this pandemic.Eric Topol (41:12):Oh, you're too kind. We'll talk again. I hope soon and great to be with you today. Thank you.Ziyad Al-Aly (41:18):Thank you.If you prefer to watch the whole convo by video, here Is the link Get full access to Ground Truths at erictopol.substack.com/subscribe

Eric Topol (00:00):Hello, this is Eric Topol, and I'm thrilled to have a chance to have a conversation with Magdalena Skipper, who is the Editor-in-Chief of Nature. And a historic note. Back in 2018, she became the first woman editor of Nature in its 149 years, and only the eighth editor of all times. Having taken over for Philip Campbell, who had been previously the editor for 22 years, we're going to ask her if she's going to do 22 or more years, but we're going to have a fun conversation because there's so much going on in medical publishing, and I think, you know, that Nature is the number one cited science journal in the world. So, welcome, Magdalena.Magdalena Skipper (00:41):Thank you very much. Real pleasure to be here and chatting with you today, Eric. Thank you.How COVID-19 Affected NatureEric Topol (00:47):Well, you know, we're still, of course, in the pandemic world. It's obviously not as bad as it had been, but there's still things going on with new variants and Long Covid, and it's not, the virus isn't going away. But first thing I wanted to get into was how did Nature handle this frenetic craziness? I mean, it was putting out accelerated publications on almost a daily or weekly basis and putting out like a speed, velocity of the likes that we've not seen. This must have been really trying for the whole crew. What, what do you think?Magdalena Skipper (01:29):It was! And, you know, the first thing I, I think I will recognize two things at the same time. So the first one, as you say, at a time, such as the pandemic, but actually at any point when there is a, a new health emergency that is spreading, especially something as unknown, as new as, as it was the case with SARS-CoV-2. And of course, in the beginning, we really knew nothing about what we were facing if speed is of the essence, but equally what's truly important is of course, the rigor itself. So that combination of needing to publish as quickly as possible, but at the same time as rigorously evaluating the papers as possible, that was actually quite a challenge. And of course, you know, what we sometimes forget when we talk about, well, researchers themselves, but also editors and publishers is of course, as individuals, as human beings.(02:33):They are going through all the trauma, all the constraints associated with various lockdowns concerns about the loved ones, perhaps those ones who are in the care. You know, in many cases of course there would've been the elderly who are individuals would've been concerned by or indeed children, because of course, schools in so many places were. And all the while, while we were dealing with these very human, very ordinary daily preoccupations, we were very focused on the fact that we had a responsibility and a duty to publish papers and evaluate them as quickly as possible. It really was an extraordinary time. And, and you know, one other thing I should emphasize is, of course, it's not just the manuscript editors who evaluate the research, it's the reporters on my team as well who are going out of their the way to find out as much information to report as robustly, find as many sources to, to interview as possible.(03:44):And, and, you know, I also have to mention colleagues who work on production side of nature actually make Naturehappen, be published online on a daily and then of course weekly basis. And literally from one week to the next all our operations had to be performed from home. And it's really remarkable that the issue was not late. We published the issue, just as you know, from as lockdowns came in. And as it happens, the production side of Nature is mainly based in, in London. So most of that team effectively found themselves not being able to go to the office effectively from one day to the next. So it really was an extraordinary time and, and a time that as I said was, was a time of great responsibility. But looking back on it, I'm actually incredibly proud of, of my team, what, what they achievedEric Topol (04:47):Did they hold up? I mean, they hadn't, they didn't get burnout from lack of sleep and lack of everything. Are they still hanging in there?Magdalena Skipper (04:55):So they are hanging in there. You'll be glad to hear. But I think, very importantly, we were there for one another insofar that we could be, of course, we were all at home remotely. We were not meeting, but we had virtual meetings, which were regular of course in as a whole team, but also in, in subgroups as we sub-teams, as we worked together, that human contact in addition to of course, loved ones and families and friends, that human contact in a professional setting was, was really, really necessary. And clearly what I'm describing was affected all of us one way or another. Sometimes there is a tendency not to remember. That also applies to editors, publishers, and of course researchers themselves. I mean, very clearly they were at the forefront of the issue facing the same problems.Nature and Challenge of Generative A.I.Eric Topol (05:57):Well, a new challenge has arisen, not that the pandemic of course has gone away, but now we have this large language models of AI, Generative AI, which you've written editorials at Nature, which, of course, is it human or is it the machine? What do you think about that challenge?Magdalena Skipper (06:19):Well of course, you know, the way I like to think about it is AI, of course, broadly is, has been around for a very long time, a number of decades, right? And steadily over the last several years, we have seen AI emerge as a really powerful and important tool in research right across a number of disciplines. The reason why we are all talking about AI right now, and I really think all of us are talking about AI all the time, is, of course, specifically the emergence of generative AI, the large language models that, that you just mentioned. And they sort of burst onto the scene for all of us really last year in the autumn with chat GPT and GPT-4 and so on. But it's important to remember that, of course, when we talk about AI, there are other models, other approaches, and machine learning in general has been creating quite some revolution in research already.(07:36): You know, probably the best example that will be familiar to many of the  listeners was of course Alpha Fold which, you know, Nature published a couple of years ago and, and has been really revolutionized structural biology. But, of course, there are many other examples which are now becoming developing much more rapidly, becoming much more, I would say, commonplace in, in research practice. You know, not just predicting structure from sequencing from sequence. And I say just so flippantly now, of course, it was such and it continues to be such an incredible tool. But of course now we have AI approaches, which actually suggest new protein design, new, new small molecule design. We've had in the last couple of years, we've had identification of new potential antibiotics that are effective against bacterial strains that have otherwise been resistant to any known antibiotics.(08:48):And, and of course, it's not just in biomedicine. Material science--I think it's very helpful, hopeful when it comes to, to AI tools as well. And then, and of course, generative AI indeed helps us in some of these contexts already. But I think your question perhaps was more focused on the publishing, the communication, the sort of output of, of research, which of course is also very important. In some way. The reason why I answered, I began to answer the question the way I did, is because I'm actually very excited about harnessing the power of AI in augmenting research itself. Helping navigate enormous data sets generate hypotheses to be tested finding new ways to advance projects. I think that's a very exciting opportunity. And we're just beginning to see the first applications of it.(10:04):Now, in terms of publishing you referred to some editorials that we wrote about this. And right at the beginning of the year, there was a flurry of excitement associated with the ability of generative AI to indeed generate text. There were some manuscripts which were published in journals that were co-authored by Chat GPT. I I even believe there was an editorial which was co-authored by Chat GPT. So in response to that, we felt very strongly that, that clearly there was a need to, to come out with a, a clear position, just as in doing research, we see AI tools as tools to support writing, but clearly they don't have the ability to fulfill authorship criteria. Clearly, they cannot be authors. Clearly, they must only remain as tools supporting researchers and individuals writing and communicating their research.(11:23):And so we, we wrote a very clear editorial about this, essentially summarizing what I just explained and asking the community to be transparent about how AI tool has been used, just as you would be transparent about your methodology, how you have arrived at the results that you're reporting and, and results that support your conclusions. So for us, it's a relatively simple set of recommendations. As I say, we ask for transparency. We understand it can be a tool that can be used to help write a paper. What we also ask at this stage that generative AI tools are not used to generate figures or images in papers, simply because there are a number of outstanding copyright issues, a number of outstanding privacy issues, they remain unresolved. And for as long as they remain unresolved, we feel it's not an appropriate application of these tools. So that's our editorial position.Eric Topol (12:42):Yeah, no, that's very helpful. I mean, where do you think, if you write a manuscript and then you put it into let's say GPT-4 and say, please edit this, is that okay? Or is that something that, and it's acknowledged that the paper was written by us researchers, but then we had it tweaked by chatbot or is that something that it wouldn't go over too well?Magdalena Skipper (13:10):Well, my preference, and actually what I would hope is that if you were writing this paper and then you felt the need to put it through a chatbot as you just put it, although I find it hard to imagine that you would find no need for that,Eric Topol (13:29):I wouldn't do it. But I know there's people out there that are working on it.Magdalena Skipper (13:32):Yeah, absolutely. But then I would hope that the last pass, the final word, would rest with you as the author. Because, of course, if you are using a tool for whatever it is that you do, you want, at the end of the day to make sure that what that tool has returned is aligned with what you intended that you perform some kind of a sense check. We, of course, all know that although GPT-4 has less of a tendency to hallucinate, so to essentially come up with fabricated sort of statements and, and reality, if you like, it remains an issue. It can remain an issue. And very clearly any, any scientific communication has to be rooted in facts. So, in the scenario that you propose, I would hope that if a researcher felt compelled to run the manuscript through a chatbot, and for example, one consideration may for an individual whose English is not their first language, who feel may feel more comfortable with a sort of support of this kind. But in the end, the final check, the final sign off, if you like, on that manuscript before submission would need to come from the researcher, from the corresponding author, from the writing group. and indeed assistance from a chatbot would need to be disclosed.Eric Topol (15:14):For us. Yeah, I mean, it's really interesting because you can almost foresee the shortcut of having to go get all the references and all the links, you could say, you know, please insert these, but you better check them because they may be fabricated Absolutely. It's going to be really interesting to see how this plays out and the difficulty of detecting what is written by a large language model versus a person.Nature and PreprintsNow another topic that I think is really in play is the preprint world and publishing via preprints. And as you know there's been Michael Eisen and the whole idea of how things would move with his journal eLife. And you will remember when you and I were together at a conference. I organized Future of Genomic Medicine many years ago at the kind of dawn of life science preprints. And some people in the audience sai, “what's a preprint?” Right? Nobody else asks about that now. It’s come a long way over this decade. And where do we go with this? Should journals like the top journals in the world like Nature require a paper to be vetted through the pre-print mechanism? Where is this headed, do you think?Magdalena Skipper (16:40):Yeah, it's an excellent question. And, and you know, by the way, I have such wonderful memories from, of that conference. I think this must have been like 11 years ago or something like that. It was a long time ago. And I actually remember presenting this, this vision of a rather radical vision of, of the future of publishing. And here we are in the future as compared to then, and we have moved relatively little by comparison to where we were then. But back to your question. So, you know, the first thing to say is that, of course, just as a reminder, preprints have been around for more than two decades now. And, and of course they initially were really spearheaded and advanced by the physical sciences community. archive itself is, as I say, more than two decades old. So, you know, for us at Nature as a multidisciplinary journal where of course, we've been publishing in the physical sciences since the very beginning of our existence as soon as preprints first emerged in those communities, we realized that we could coexist very harmoniously as a journal peer-review based journal with preprints.(17:59):So when initially biological sciences community embraced them and bioRxiv was established, and then of course, many other archives and then subsequently actually really spearheaded by Covid, the medical and clinical community began to embrace preprints. in many ways, for us, that was nothing new. It was just an extension of something that we worked with before. Although our own our own policies have evolved. So, for example, during the pandemic we actually mandated deposition of papers that were submitted to us that were Covid related. We mandated the deposition in a preprint server. The authors had the choice which server they deposited, but we wanted those manuscripts to be available to the community for the scrutiny as soon as they were finalized, as soon as they were actually written. So while we were reviewing them again as quickly as rigorously, but as quickly as possible, the preprint was already available for the community just before the pandemic.(19:17):As it happens, we also took a step forward with our policy. So previously, let's just say we were completely fine with preprints. We saw preprints as compatible with submission to, to Nature, and for that matter to the other journals in the Nature Portfolio. But actually just in the year before COVID started, we decided to actively encourage our authors to deposit preprints. We could see that preprint sharing had great advantage. You know, the, the usuals of advantages, which are often listed first are of course ability to make that primacy claim, make a stake that, that you have been working on something and, and this is your project. You have a set of results that you are ready to communicate to, to the community at large. And of course, another very important one is that sort of community and, and almost public form of peer review and, and ability to comment.(20:30):And incidentally, I remember as you know, my, my history as an editor very well. We've known each other for a long time. I remember when the genomics community, which is sort of my, my background is sort of my old hat, if you like, that, that I used to wear when the genomics community began to embrace preprints especially the population and evolutionary genomicists really embraced this idea that this was like a group peer review. And the authors of those preprints were very grateful to the community for improving the papers before they were submitted to journals, or sometimes that sort of community review was going on while a paper was being considered at a journal. And we, as editors actually encouraged sort of formal submission of these reviews, if you like, I mean, formal maybe is the wrong word, but we were saying that we would take those comments into account when evaluating papers.(21:38):So there has been an interesting evolution that more and more disciplines, more and more fields have embraced preprints as a way of disseminating information. Preprints service themselves have also grown and matured in the sense that there is now realization that, for example, clinical preprints need a higher degree of scrutiny they're posted on a preprint server than maybe let's say theoretical physics or theoretical biology preprints. So overall all communities collectively have grown and matured. Where are we going with this? I mean, who knows? I was predicting 12 years ago you know, a bit of a different, more advanced future today. It's very difficult to predict the future. I do think, however, that what we are seeing today, that sort of hand in glove coexistence of preprints with journals, with peer reviewed papers is going to continue into the future. And I think actually that's a really valuable and interesting combination. So it's a great development to see and great to see that communities right across disciplines have really embraced this.Eric Topol (23:11):Yeah, I think it does complement, obviously the traditional peer review of a few expert reviewers with, you know, could be hundreds if not thousands of people that weigh in on, on a pre-print. So yeah, it's fascinating to see. And it's, I still remember the vision that you portrayed for it, and how we we're not quite there yet, but I'm sure there'll be further evolution.Women in Science: Where Do We Stand?Now, another area that I think is particularly good to get your input, because you're a woman in science, as you mentioned, you know, grounded obviously in genetics and genomics, and here you are, one of the most influential women in science at a time when there's been a reckoning that women in science have been shortchanged historically, I mean, for hundreds of years. Do you see that this is starting to get better? Are there palpable signs that we're finally getting kind of equal rights here? Or are we, is it, is it just still a long fight ahead?Magdalena Skipper (24:20):So the, the optimist in me and, and I should say, you know, my, my glass, my glass is always half full. The optimist in me says that it is getting better, but the realist in me has to add immediately that the changes too slow. It really is too slow. We do see many more women prominently able to make the contributions that they should, they can, and they should make to whatever discipline whatever aspect of the research community and beyond they wish to, to make. I still think it costs them too much. I still think we don't appreciate and support women sufficiently.(25:23):Maybe we have moved on the bottleneck in the, in the pipeline a little bit further, towards more seniority. But we still, we still don't sufficiently support women. As I say, we, I think we still default to an expectation that successful women in science in research more broadly will somehow emulate how success has looked in the past. And that's a shame, that's a shame not just for those women who are trying to come in and make a difference, but it's a shame for all of us because it means that we are denying diversity in that picture of success. Yes. So yes, I think, I think that we have seen many changes, but I think the change is not happening fast enough.Eric Topol (26:23):Yeah. One of the things that I've noticed since of particular interest in AI is that the very profound imbalance of researchers, the gender imbalance there is just, you know, I'm not even sure if it's 10% women researchers in AI, so that has to be changed. And so this, there's so many things that are holding us back, but, but that's certainly one of, of many.Magdalena Skipper (26:49):Absolutely. And, and, and if I can just add, there are some outstandingly influential female researchers in the AI field, as you say, they are just outnumbered. Yes. , I think not given the opportunity to, to fully blossom, if you like, considering their capabilities and, and their contributions already.Eric Topol (27:11):You know, it's so true. I just interviewed Melanie Mitchell from the Santa Fe Institute, and I work with Fei- Fei Li. And when I, when Fei-Fei Li and I spoke some months ago about a book (Genius Makers) that Cade Metz, the New York Times journalist had written, and I say, why didn't he bring up or emphasize the role of any women in the whole book . Yes--who work in A--I mean, she, she obviously was, was did not take that particularly well, and as did I.Too Many Nature Portfolio Journals?So one of the other areas that I think you already touched on, which is separating Nature, the flagship journal from the Nature Portfolio of, I don't know what it's up to now, 200, 300, I'm not sure how many journals are. So do you, do you have to over oversee that? Do you have input on that? Because what I worry about is, you know, people quote a Nature journal and it may not be, you know, at that level that you would be proud of. What, what are your thoughts about this endless proliferation of the nature portfolio?Magdalena Skipper (28:17):Well, I, I'm, first of all, I'm not sure if it's endless, butEric Topol (28:20):Oh, that's good. .Magdalena Skipper (28:22):So, so  let me, I think in your question, you touched on a number of things. So first of all, a clarification. So my role is as Editor-in-Chief of Nature, and of course, that is my main focus. there is another aspect to my role, which is Chief Editorial Advisor for the Nature Portfolio. So in that sense each of the journals within the Nature portfolio has its own chief editor. but by virtue, I guess, of my seniority, and also by virtue of multi-disciplinarity of Nature I have this advisory role to my colleagues in the other journals. I like to think about the Nature Portfolio as an ecosystem, actually. And it's an ecosystem, like any ecosystem. It has different niches, each of which fulfills a different role. Some of them are bigger, some of them are smaller, some of them are very specialized, others are more general.(29:22):And I think you know, working with researchers for many years as an editor now, I can see benefits to having that sort of almost an ecosystem type approach to publishing. You know, for example, we mentioned already earlier that in my previous sort of incarnation as an editor, my focus was on genomics especially in the context of human genomics. of course starting from the Human Genome Project, these were very large or have, where, why, why am I using past tense? They are, to this day, very large collaborative projects involving many different labs, many different approaches these days that they're not just focused on genomics, but of course other omics go hand in hand with them. So when a project comes to fruition, when, when it comes to be published, there are many different pieces that need to be communicated, many different papers of different sizes of different value.(30:32):And for example what value maybe is the wrong word of different utility? So, for example, there may be a flagship paper that is published in the pages of my journal of Nature, but there may be papers that specifically described development of methodology that was part of the same stage of the project. And those papers may be published in Nature Methods, which is part of the Nature Portfolio. There are other journals that are part of Nature Portfolio, which have different editorial bar. And so, you know, one example is Scientific Reports, which is a journal which does not require conceptual novelty in the papers that it publishes. Of course, it requires rigor and, and robustness in the papers that it publishes, like every journal should. But there is utility in publishing papers in a journal like this.(31:36):There may be replications that are published there that further add further evidence to support conclusions that are already well known, but nevertheless, they're useful. I should however, add that in Nature itself, we also publish replications, right? There are different degrees of influence and impact that, of course, different studies be there, replications or not that can carry. So, that will be my way of conceptualizing the Nature Portfolio. and, you know, coming back to your, to your comment that it seems like it's endless. I think well, nothing, nothing is endless. Of course. Nothing, nothing, right, grows forever. I do think that we have in the launches within the portfolio, we have been able to capture and at the same time serve an interesting evolution in the research ecosystem itself. So the final comment I will make on this is, if you look at some of the more recent launches in the portfolio, they've been what we like to call thematic journals, such as, for example, Nature Food or Nature Water.Eric Topol (33:10):Right?Magdalena Skipper (33:10):And here we are really capitalizing on that multi-disciplinarity of these emerging themes that, especially in the context of sustainable development goals, have acquired their own identity. They don't belong to one discipline or another discipline. And, and so these journals, they're new journals, relatively new journals, some of them very new Nature Waters is, is quite new, but they provide a focal point for researchers who come together to solve a particular set of problems from different disciplines. And I think that's an interesting function in, as I say, for the community.What About the Paywalls?Eric Topol (33:53):Yeah, there's no question some of the newer journals and their transdisciplinary mission --they're needed and they become extremely popular and well -cited very quickly to prove that. So along that line obviously the public is all fired up about paywalls and you know, and obviously for Covid, there was no paywalls, which is pretty extraordinary. Do you see someday that journals will have a hard time of maintaining this? I mean, you have what I consider an extraordinary solution, which is the ReadCube postings anyone can access, you just can't download the PDF, and I wish authors would always routinely put that out there because that would solve part of the problem. But do you think we're going to go to a free access that's much more wide, perhaps even routine, in the years ahead?Magdalena Skipper (34:52):So certainly open access as in ability to access a manuscript, published manuscript without any payment or barrier associated with a Creative Commons license is something that is advanced as a, as a preferred future by many researchers, by many funders. and for that matter, actually many publishers as well. You know, let me make one thing very clear. As an editor, I would love as many people as possible to read the papers that I publish in my journal.Magdalena Skipper (35:30):That should go without saying. Sure. at the same time, publishing papers, of course, is associated with a cost, and, and that cost has to be somehow covered. In the old days it was exclusively covered by library subscriptions or site licenses or personal subscriptions. Now the focus is shifting. And of course, Nature itself as well as the other research journals such as, for example, Nature Medicine or indeed Nature Water, as I mentioned before are what we call transformative journals. So effectively we are hybrid journals that advocate for open access. So today, when you submit a paper to Nature, you can publish under the traditional publishing model, or you can choose to publish open access, which is associated with an article processing charge. That should, in my view, be part of your costs of doing research, because after all, I'm a firm believer in the fact that publishing your research should be seen as part of doing research, not sort of an add-on.(36:47):Now, I'm glad you mentioned read Read Cube and this functionality that we call shared it. We developed it actually quite some years ago. I would say at least a decade ago. it remains curiously underappreciated. Yeah. I just don't understand it. Yeah, exactly. And, and we, we inform the authors that they are free to use that link. And, and just to clarify, it's a linked as you exactly as you explained to an online version of the paper. It's the final version, the record version of the paper. You can't download it, but you can share that link. Anyone can share that link once they have it Infinite number of times. So it's not like the link expires, or it's a, a finite number of, of that it has a number of finite number of uses in addition to that nature.(37:49):And for that matter, the whole of Springer Nature is part of Research4Life. Now, that's an organization that provides free access to all content from publishers. And Springer Nature is not the only publisher that's part of Research for Life that provides full access to all of our content in the countries which are designated as low and middle income countries by the World Bank. So that we've been part of that. And, and previously for many, many years, in fact, decades, again, that is curiously underappreciated, including in the low and middle income countries. So, you know, recently had an opportunity to do some visits in Africa. And my, my take home message there was, if there is one thing that you remember from our conversation or from my presentation, please remember about Research4Life.Magdalena Skipper (38:52):Because that content is freely available if you follow, if you go to our content through Research4Life. And incidentally, there's also training, which is available there. So part of Nature portfolio in addition to journals, we have Nature Master classes, which is training for researchers. And that is also completely freely available in those countries. So there are a number of approaches to, to getting content open access is definitely growing, but there are those other ways to gain access to content which is not open access at the moment.Eric Topol (39:33):I'm really glad you reviewed that because a lot of people who are going to be listening are going to really cue into that. Now the last question for you is, you know, it's not just every Wednesday, 51 or whatever, 50 weeks a year, that you're getting the journal ready, but it's every day now that you're putting out stuff and on the Nature website. Features that are by the way, free or full access and many other things to keep Nature out there on a daily, if not minute to minute basis. So this is really a big charge to, you know, do this all so well. So what keeps you up at night about Nature is this, this must be a very tough position.Magdalena Skipper (40:28):So the first thing I would say that is that of course it's, it's not me. I'm just the person here talking to you representing Nature. I have an outstanding team.Eric Topol (40:44):I've met them, and they're amazing.Magdalena Skipper (40:46):And it's really them who are making it possible on a minute by minute, certainly day by day basis. And so the reason why I sleep relatively well is thanks to them actually, okay,Eric Topol (41:00):. Okay.What Keeps You Up At Night?Magdalena Skipper (41:01):But more, but more broadly. and this is a thought which is bigger than Nature itself. What actually keeps me up at night these days is the rather difficult light in which science and research is portrayed these days increasingly.Magdalena Skipper (41:27):And I think it's very unfortunately being to support other goals and other ends forgetting about the fact that science is an ongoing process that science takes steps back when it needs to revise its position, that it still continues to be true, that s science progresses through self-correction. Even if that self-correction doesn't happen overnight, it takes time to realize that a correction is required, takes time to evaluate judiciously that correction is required and what kind of correction is required, right? These are the things that of course, you and I know very well. But the, sometimes if for individuals who are not close to the process of how science research fact-based discovery is conducted, if you just look at information on social media or in general media, you may walk away with an impression that science is not worth paying attention to that science is in some deep crisis.Magdalena Skipper (43:04):And I think that's, that's a shame that that's a picture that we have other things that need other things in science, in research that need correcting, that need sorting out. Of course, we mustn't forget that research is done by humans and, and after all it is human to air. But overall, that's actually something that keeps me up at night. That overall, I really hope that those of us who are engaged in one way or another within the research enterprise, we can continue to advance the right kind of image that it's not perfect in some artificial way, but actually, at the same time, it's the only way that we can move forward. We can understand the world around us, and we can wake, make the world around us better, actually.Eric Topol (44:11):Yeah. I'm so glad you've emphasized this because just like we talked earlier about distinguishing between human and AI content generated here, we have science and anti-science blurring facts, blurring truths, and basically taking down science as a search for truth and making it trying to, you know, obscure its mission and, in many ways, we, we saw it with not just anti-vax, but it's much bigger. The political motives are obvious extraordinary, particularly as we see here in the U.S. but other countries as well. So I almost didn't hit you for that question, just because it's so profound. We don't have the answers, but the fact that you're thinking about it tells, tells us all a lot. So Magdalena, this has been a joy. I really appreciate all your candid and very thoughtful responses to some of these questions.(45:09):Some of them pretty tough questions I have to say. And I look forward to our conversations and chances to visit with you again in the future. And congratulations again on taking on the leadership of Nature for five years now-- I believe just past your five-year anniversary now. You could say that's small out of 155 years, but I think it's a lot. particularly since the last few years have been, you really challenging. But to you and your team ultimately –-major kudos. I'm on the Nature website every single day. I mean, even, I when I’m on vacation, I'll be checking out the Nature site. So you can tell that I think so highly of the its content and we'll look forward to future conversations going forward.Magdalena Skipper (45:52):Thank you very much. Thank you very much, Eric. It's always a pleasure to talk to you. Thank you. Get full access to Ground Truths at erictopol.substack.com/subscribe

Transcript Eric Topol (00:00):This is a real great opportunity to speak to one of the most impressive medical informaticists and leaders in AI in the United States and worldwide. Dr. John Halamka, just by way of background, John, his baccalaureate in Stanford was at U C S F/Berkeley for combined MD PhD trained in emergency medicine at U C L A. He went on to Harvard where he, for 20 years was the Chief Information Officer at Beth Israel Deaconess. And then in 2020 he joined Mayo Clinic to head its platform to help transform Mayo Clinic to be the global leader in digital healthcare. So welcome, John. It's so great to have you. And by the way, I want to mention your recent book came out in April, one of many books you've written, redefining the Boundaries of Medicine, the High Tech High Touch Path into the Future.John Halamka (01:00):Well, a thrilled to be with you today, and you and I need to spend more time together very clearly.Eric Topol (01:06):Yeah, I really think so. Because this is the first time we've had a one-on-one conversation. We've been on panels together, but that's not enough. We've got to really do some brainstorming, the two of us. But first I wanted to get into, because you have been on a leading edge of ai and Mayo is doing big things in this space, what are you excited about? Where do you think things are right now?John Halamka (01:35):So you and I have been in academic healthcare for decades, and we know there's some brilliant people, well-meaning people, but sometimes the agility to innovate isn't quite there, whether it's a fear of failure, it's the process of getting things approved. So the question of course is can you build to scale the technology and the processes and change policies so that anyone can do what they want much more rapidly? And so what's been exciting over these last couple of years at Mayo is we started with the data and we know that anything we do, whether it's predictive or regenerative, starts with high quality curated data. And so by de-identifying all the multimodal data of Mayo and then working with other partners around the world to create a distributed federated approach for anyone to train anything, suddenly you're empowering a very large number of innovators. And then you've seen what's happened in society. I mean, culturally, people are starting to say, wow, this ai, it could actually reduce burden, it could democratize access to knowledge. I actually think that yes, there need to be guidelines and guardrails, but on the whole, this could be very good. So here we have a perfect storm, the technology, the policy, the cultural change, and therefore these next couple of years are going to be really productive.Implementing a Mayo Randomized AI TrialEric Topol (02:59):Well, and especially at Mayo, the reason I say that is not only do they recruit you, having had a couple of decades of experience in a Harvard program, but Mayo's depth of patient care is extraordinary. And so that gets me to, for example, you did a randomized trial at Mayo Clinic, which there aren't that many of by the way in AI where you gave E C G reading power of AI to half the primary care doctors and the other half you didn't for determining whether the patients had poor cardiac function that is low ejection fraction. And now as I understand it, having done that randomized trial published it, you've implemented that throughout the Mayo Clinic system as far as this AI ECG support. Is that true?John Halamka (03:56):Well, right, and let me just give you a personal example that shows you how it's used. So I have an SVT [supraventricular tachycardia] , and that means at times my resting heart rate of 55 goes to one 70. It's uncomfortable. It's not life-threatening. I was really concerned, oh, may I have underlying cardiomyopathy, valvular disease, coronary artery disease. So Paul Friedman and Peter Newsworthy said, Hey, we're going to take a six lead ECG wearable, send it to your home and just record a bunch of data and your activities of daily living. And then we buy 5G cell phone. We'll be collecting those six leads and we'll run it through all of our various validated AI systems. And then we'll tell you based on what the AI suggests, whether you're at high risk or not for various disease states. So it says your ejection fraction 70%. Oh, good. Don't have to worry about that. Your likelihood of developing AFib 3% cardiomyopathy, 2% valvular disease, 1%. So bottom line is without even going to a bricks and mortar facility here, I have these validated algorithms, at least doing a screen to see where maybe I should get additional evaluation and not.Eric Topol (05:12):Yeah, well see what you're bringing up is a whole other dimension. So on the one hand that what we talked about was you could give the primary care doctors who don't read electrocardiograms very well, you give them supercharged by having a deep learning interpretation set for them. But on the other, now you're bringing up this other patient facing story where you're taking a cardiogram when somebody's perfectly fine. But from that, from having deep learning of cardiograms, millions of cardiograms, you're telling what their risks are that they could develop things like atrial fibrillation. So this is starting to span the gamut of what the phase that we went through or still going through, which is taking medical images, whether it's a cardiogram or a scan of some sort, and seeing things with machines that humanize really can't detect or perceive. So yeah, we're just starting to get out of the block here, John. And you've already brought up a couple of major applications that we were not even potentially used three, four or five years ago that Mayo Clinics leading the charge, right?The Power of Machine EyesJohn Halamka (06:26):Well, yeah, and let me just give you two quick other examples of these are in studies now, right? So they're not ready for active patient use. The animate GI product does an overread of endoscopy. And what we're finding is that the expert human, I mean anywhere in the world, expert humans miss about 15% of small polyps. They're just hard to see. Prep may not be perfect, et cetera. The machine misses about 3%. So that's to say a human augmented with overread is five times better than a human alone pancreatic cancer, my father-in-law died about 11 years ago of stage four pancreatic cancer. So this is something that I'm very sensitive about, very often diagnosed late, and you can't do much. What we've been able to see is looking at pancreatic cancer, early films that were taken, abdominal CT scans and these sorts of things, algorithms can detect pancreatic cancer  two years before it is manifested clinically. And so here's the ethical question I'll pose to you. I know you think about a lot of this Scripps Mayo, UCSF, Stanford, we probably have thousands and thousands of abdominal CTs that were read normal. Is it an ethical imperative as these things go through clinical trials and are validated and FDA approved to rerun algorithms on previous patients to diagnose disease we didn't see?Eric Topol (08:03):Well, that is a really big important question because basically we're relieving all this stuff on the table that doesn't get diagnosed, can't be predicted because we're not even looking for it. And now whether it's retina, that is a gateway to so many systems of the body, or as you're mentioning various scans like an abdominal CT and many others that like mammography for heart disease risk and all sorts of things that weren't even contemplated that machine eyes can do. So it's really pretty striking and upending cancer diagnosis, being able to understand the risk of any individual for particular types of cancer so that you can catch it at the earliest possible time when it's microscopic before it spreads. This, of course, is a cardinal objective. People don't die of cancer per se. They die of its metastasis, of course, for the most part. So that gets me now to the next phase of ai because what we've been talking for mostly so far has been what has been brewing culminating for the last five years, which is medical images and what, there's so many things we can glean from them that humans can't including expert humans in whatever discipline of medicine.Multimodal AI and Social Determinants of Health(09:19):But the next phase, which you are starting to get at is the multimodal phase where you're not just taking the images, you're taking the medical records, the EHRs, you're getting the genomics, the gut microbiome, the sensors. You mentioned one, an ECGs, a cardiogram sensor, but other sensors like on the wrist, you're getting the environmental things like air pollution, air quality and various things. You're getting the whole ball of wax any given individual. Now, that's kind of where we're headed. Are you doing multimodal ai? Have you already embarked in that new path? Now that we have these large language modelsJohn Halamka (10:02):And we have, and so like anything we do in healthcare innovation, you need a Pareto diagram to say, what do you start with and where do you go? So in 2020, we started with all of the structured data problems, meds, allergies, labs. Then we went to the unstructured data, billions of notes, op reports, H and Ps, and then we moved to telemetry, and then we moved to CT, MRI, PET. Then we move to radiation oncology and looking at all the auto contouring profiles used in linear accelerators and then to omic, and now we're moving to an inferred social determinants of health. And let me explain that for a minute.(10:45):Exposome, as you point out, is really critical. Now, do you know if you live in a Superfund site area, do you know what risks you might have from the PM 2.5 particulates that are blowing through San Diego? Probably you don't. So you're not going to self-report this stuff. And so we have created something called the house Index where we've taken every address in the United States, and based on the latitude and longitude of where you live, we have mapped air, water, land, pollution, access to primary care, crime, education, grocery stores, stores, and therefore we can infer about 40 different things about your expose em just from where you live. And that's a mode. And then as you say, now, starting to gather remote patient monitoring. We have this acute advanced care in the home program where we're taking serious and complex illness, caring for the patient in the home, starting to instrument homes and gather a lot more telemetry. All of that multimodal data is now available to any one of the 76,000 employees of Mayo and our partners for use in algorithm development.Eric Topol (11:58):Yeah, no, that's extraordinary. And I also would say the social determinants of health, which you've really gotten into as its importance. There are so many papers now over the last several years that have emphasized that your zip code is one of the most important things of your health. And it's not even just a zip code. It's your neighborhood within that zip code for the reasons that you've mentioned. And inferring that and imputing that with other sources of data is vital. Now, this multimodal, you've again anticipated one of my questions, the possibility that we can gut hospitals as we know them today. Yes, preserving the ICUs, the emergency departments, the operating rooms, but those other people that occupy the vast majority of beds in the hospital that are not very sick, critically Ill. Do you think we're going to move as you're innovating at Mayo whereby we'll be able to keep those people at home for the most part in the years ahead? I mean, this isn't going to happen overnight, but do you think that's where we're headed?The Hospital-at-HomeJohn Halamka (13:08):So to date, Mayo and its partners have discharged about 23,000 patients from their homes. And as you can guess, we have done clinical trials and deep dive studies on every one of the patient's journeys. And what have we seen across 23,000 patients? Well, so generally, about 30% of patients that present for acute care to an emergency department come in by ambulance are appropriate for care in non-traditional settings. I mean, I think you would agree, somebody with episodic ventricular tachycardia, you're probably not going to put in a home setting, but somebody with congestive heart failure, COPD, pneumonia, I mean, these are things that, as you say, if they're going to get sicker, it will be over hours, not minutes. And therefore you can adjust in these molar than 20,000 patients. What we've seen is the outcomes are the same, the quality is the same safety, the same patient satisfaction. You get net promoter scores in the mid-nineties. You find me a hospital with a net promoter score in the mid nineties. You're eating your own food, slipping your own bed. Oh, your granddaughter's coming at 2:00 AM on a Sunday, whatever. And then ask yourself this other question, nosocomial infections,Eric Topol (14:31):Right?John Halamka (14:31):How many methicillin resistant staph infections do you have in your office? You're like, none, right? So you're infections in fall, so okay, better, stronger, cheaper, faster. And the safety of the quality are that for about 30% of the population should be a standard of care.Eric Topol (14:56):That's really big. So you don't think we have to do randomized trials to prove it?John Halamka (15:01):I mean, we have done enough studies to date, and there are organizations, Kaiser Permanente, Cleveland Clinic, all these folks who are joining us in investigating these areas. And the data is very compelling.Patients Asking Questions to LLMsEric Topol (15:17):Yeah, that's really exciting. And we may be able to jump past having to go through the large trials to prove what you just reviewed. So that's one thing of course that we're looking for in store. Another is the patient doing advanced large language model searches. So as you and everyone knows, we've done Google searches for years about symptoms, and inevitably people come up with hypochondria because they have some horrible disease that they looked up that is not a very good match specific for their condition and their background. But soon already today, we have people going into being creative mode, G P T four and other searches, and they're getting searches about their diagnosis and about what's the best literature and best treatments and expectations. That won't be FDA regulated. We don't have regulation of Google searches. So how do you see the democratization of large language models with patients having conversations with these chatbots?John Halamka (16:32):And of course, you ask a question no one has answered yet, but here are a few threads. So we know the challenge with existent commercial models as they're trained on the public internet. Some are trained on additional literature like PubMed or a mimic dataset, but none are trained on the rich clinical experience of millions and millions of patients. So therefore, they don't have the mastery of the care journey. So question, we are all asking, and again, no one knows. Then you take a GPT, BARD, a MedPaLM and additional pre-training with rich de-identified clinical experience and make it a better model for patients who are going to ask questions. We've got to try and we've got to try within guardrails and guidelines, but we definitely want to explore that. Can you or should you train a foundational model from scratch so that it doesn't have the bias of Reddit and all of the various kinds of chaff you find on the public internet? Could be very expensive, could be very time consuming. Probably society should look at doing it.Eric Topol (17:50):So this is just a review for those who are not up to speed on this, this means setting up a base model, which could be 20 to 30,000 graphic processing units, big expense. We're talking about tens of millions, but to do it right, so it isn't just a specialized fine tuning of a base model for medical purposes, but something that's de novo intended that no one's done yet. Yeah, that's I think a great idea if someone were to go down that path. Now you, early on when we were talking, you mentioned partners, not just other health systems, but one of the important partners you've established that's been out there as Google, which I think set up shop right in Rochester, Minnesota, so it could work closely with you. And obviously they have MedPaLM2, they have BARD, they published a lot in this space. They're obviously competing with Microsoft and others, but seems like it's mainly an arms race between those two and a few others. But how is that relationship going? And you also were very right spot on about the concerns of privacy, federated ai, privacy computing. Can you tell us about Mayo and Google?What is the Collaboration Between Mayo and Google?John Halamka (19:06):Well, absolutely. So Google provides storage, compute, various kinds of tools like their fire engine for moving data between various sources. Google does not have independent access to any of Mayo's data. So this isn't a situation of we have a challenging medical or engineering problem, bring 60 Google engineers to work on it. No, what they mean is they help us create the tooling and the environment so that then those with permission, Mayo employees or Mayo's partners can work through some of these things and build new models, validate models. So Google has been a great enabler on the tool set and building scale. You probably saw that Eric Horvitz gave a recent grand rounds at Stanford where he explained scale makes a difference, and that you start to see these unexpected behaviors, this emerging goodness, when you start dealing with vast amounts of multimodal data, vast amounts of compute. And so working with a cloud provider is going to give you that vast amounts of compute. So again, privacy, absolutely essential, de-identify the data, protect it, control it, but you can't as an institution, get enough computing power locally to develop some of these more.Towards Keyboard Liberation and Machine Chart ReviewEric Topol (20:36):Well, that goes back to the dilemma about building a base model with just the capital costs no less. You can't even get these GPUs scale because their supply and demand mismatch is profound. Well, the other thing, there's two other areas I want to get your impressions about. One of course is the change of interactions with patients. So today, as you well know, having all these years overseeing the informatics, Beth Israel now Mayo, the issue of the keyboard and the interference that it provides, not just as a data clerk burden to clinicians, which is horrible for morale and all the hours even after seeing patients that have to be put into charting through the EHRs and these clunky software systems that we are stuck with, but also the lack of even having face-to-face eye contact with patients in that limited time they have together. Now, there are many of these so-called ambient AI language, natural language processing, using large language models that are of course turning that conversation not just to a remarkable note, but also of course any part of the note, you could go back to the raw conversation. So it has trust embedded as what was really said. And then you have all these downstream functions like prescriptions, follow-up appointments, nudges to the patients about whatever, like their blood pressure or things that were discussed in the visit. You have translation to the patient at their level of education so they can understand the note you have things that we never had before. You have orders for the test or follow up appointment pre-authorization. What about these, John, are these the real deal or are we headed to this in the near term?John Halamka (22:41):So 10 years ago, I said all of these meaningful use criteria, all the keyboarded data entry, structured data and vocabularies. What if you had the doctor and the patient had a conversation and the conversation was the record? That was the legal record. And then AI systems extracted the structured data from the conversation. And there you would have satisfaction by both patient and doctor and a very easy source of truth. Go back to what was said. And of course, 10 years ago everyone said, that'll never happen. That's too far.(23:20):And so I'll give you a case. My mom was diagnosed with a brain abscess about a year ago. She's a cure of the brain abscess. I with ambient listening, had a conversation with my mother and it went something like this. Yes, I started to develop a fever. I said, oh, and you live alone, right? Oh, yes. My husband died 13 years ago. The note comes out, the patient is an 81 year old widow. So we're having a conversation about my father dying and she lives alone. And I didn't use the word widow, she didn't use the word widow. And so what it shows you is these systems can take detailed conversation, turn them into abstract concepts and record them in a way that's summarized and meaningful. Last example I'll give you recently, I did grand rounds at Mayo and I said, here's a challenge for all of us. It's Sunday at three in the morning. Mrs. Smith has just come in. She has a 3000 page chart, 75 hospitalizations and four or visits. Her complaint tonight is, I feel weak,Eric Topol (24:38):Right? That's a classic.John Halamka (24:43):How are you going to approach that? So we have an instance of MedPaLM2 that is containerized. So that I was able to put a prompt in it with some background data without, and it was all de-identified, but it was all very secure. So I put the 3000 pages into this MedPaLM2 container and said, audience, ask any question that you want. Oh, well, what medication should she be taking? What's her follow-up plan? Were there any complications in any of her surgeries? And within seconds, every answer to every question just appears. They say, oh my God, I can now treat the patient. And so this is real. It is absolutely. It's not perfect, but give us a couple of quarters.Eric Topol (25:31):Yeah, quarters not even years. I think you're putting the finger on something that a lot of people are not aware, which is when you have complex patients like what you just described, that woman, and you have so much information to review, no less the corpus of the medical literature, and you have help with diagnosis treatments that you might not otherwise thought of. It also gets me back to a point I was going to make the machine vision during colonoscopy where it does pick up these polyps, but it was shown that at the end of the day in the afternoon for gastroenterologists that are doing colonoscopies all day, their pickup rate drops down. They get tired, their eyes are just not working as well. And here your machines, they don't get tired. So these things are augmenting the performance of physicians, clinicians across the board potentially.(26:28):And yes, there's a concern as you touched on about confabulation or hallucinations, whatever, but this is a work in progress. There will be GPT-X, BARD-15 or whatever else right now, another area that is hot, which is still very in the earliest nascent stage, is the virtual medical coach. Whereby any of us with all our data, every visit we've ever had, plus our data that's in real time accruing or scans or slides or whatever it is, is all being fed in process with the medical literature and helping us to prevent a condition that we would have high risk to develop or manifest or better management of the various things we do have that we've already declared. What about that, John? Are we going to see virtual medical coaches like the kind we see for going to the airport, or you have an appointment such and such about your daily life, or is that something that is way out there in time?John Halamka (27:37):I know you're going to hate this answer. It depends.Eric Topol (27:41):Okay. I don't hate that. I like it actually. Yeah.John Halamka (27:44):So some years ago, one of my graduate students formed a virtual coaching company, and what he found was patients would often start with a virtual coach, but they wouldn't stick with it because the value add wasn't necessarily there. And that is it wasn't then every day there was something new or actionable. And so if it's few and far between, why do you want to go through the effort of engaging in this? So I think our answer there is we need to make sure that the person who uses it is getting something of value for using it. Reduced insurance rates, free club memberships to a gym, whatever, something of value. So it gets some stickiness.Virtual AI CoachingEric Topol (28:33):Yeah. Well, it's still early and right now, as you well know, it's really confined to certain conditions like diabetes or depression or high blood pressure. But it certainly has the chance in the years ahead to become broad for any individual. And that gets back to the patient scenario that you presented where you had all the data of that woman who presented with weakness as the inputs. And just think about that happening in real time, giving feedback to any given individual, always thinking that it's optional. And as you say, maybe it'd be more elective. There were incentives, and if people don't want it, they don't have to use it, but it's something that's out there dangling as a potential. Well, of the things we've discussed, there are many potential ways that AI can be transformative in the future, both for clinicians, for health systems, for patients. Have I missed anything that you're onto?John Halamka (29:40):Just that in predictive AI, we can judge performance against ground truth. Did you have the disease or not? Did you get a recommendation that was followed up on and it was positive? With generative ai measuring quality and accuracy, doing follow up and oversight is much harder. So I think what you're going to see is FDA and the office of the national coordinator and the White House work through generative AI oversight. It's going to start with, as we've seen voluntary oversight from some of the companies themselves. And it will evolve into maybe some use cases that are considered reasonable practices and others that we defer reasonable practices. Hey, you want an agent that will pre-draft your email and then you just edit it, that's fine. And Mayo is live with that in Epic inbox. How about help you write a letter or help you take, as you say, a very complex medical condition, explain it in eighth grade English or a foreign language. Very good at all of that differential diagnosis, not quite ready yet. And so I think we'll start with the administrative use cases, the things that reduce burden. We'll experiment with differential diagnosis. And I don't think we yet have line of sight to say, actually, we're going to have the generative ai do your diagnosis(31:09):Not there yet,Machines Promoting EmpathyEric Topol (31:10):Right? Perhaps we'll never be, particularly for important diagnoses, maybe for routine things that are not a serious matter. One thing that I didn't anticipate, and I want to get your view. When I wrote deep medicine, I was talking about restoring the patient-doctor relationship and the gift of time that could be garnered from having this machine support. But now we're seeing the evidence that the AI can promote empathy. So for example, reviewing a doctor's note and telling the doctor, you didn't show you're very sensitive. You weren't listening, making suggestions for being a more empathic physician or nurse. Did you foresee that too? Because you've been ahead of the curve on all this stuff.John Halamka (32:04):So here's an interesting question. You and I are physician, scientist, writers. How many physician scientist writers are there? Not so many. So what you get are brilliant math or brilliant science, and it is communicated very badly. So I did not anticipate this, but I'm saying the same thing you are, which is you can take a generative AI and take something that is not very digestible and turn it to something highly readable. And whether that's empathy or clarity or whatever, it actually works really well.Eric Topol (32:43):Yeah. Yeah. I mean, I kind of stunned by this because the machines don't know empathy. They can't feel empathy, but they can promote it. And that's really fascinating. So this has been an uplifting discussion. A lot of the things that's happening now give credit to you that you saw coming long before others, and it's a real joy. So we got to keep up with each other. We got to do some more brainstorming on the things that we haven't discussed today. But thanks so much, John, for joining me and for being such a bright light for the work you're doing with Mayo Clinic as a president of its platform. That's no question. Transforming the future of healthcare.John Halamka (33:25):Well, hey, thanks for having me. And I would say both you and I have taken the digital Hippocratic Oath. We will do no digital harm.Eric Topol (33:33):Love it. Get full access to Ground Truths at erictopol.substack.com/subscribe

Transcript with LinksEric Topol (00:00):This is Eric Topol, and I'm so excited to have the chance to speak to Melanie Mitchell. Melanie is the Davis Professor of Complexity at the Santa Fe Institute in New Mexico. And I look to her as one of the real, not just leaders, but one with balance and thoughtfulness in the high velocity AI world of large language models that we live in. And just by way of introduction, the way I got to first meet Professor Mitchell was through her book, Artificial Intelligence, A Guide for Thinking Humans. And it sure got me thinking back about four years ago. So welcome, Melanie.Melanie Mitchell (00:41):Thanks Eric. It's great to be here.The Lead Up to ChatGPT via Transformer ModelsEric Topol (00:43):Yeah. There's so much to talk about and you've been right in the middle of many of these things, so that's what makes it especially fun. I thought we'd start off a little bit of history, because when we both were writing books about AI back in 2019 publishing the world kind of changed since . And in November when ChatGPT got out there, it signaled there was this big thing called transformer model. And I don't think many people really know the difference between a transformer model, which had been around for a while, but maybe hadn't come to the surface versus what were just the deep neural networks that ushered in deep learning that you had so systematically addressed in your book.Melanie Mitchell (01:29):Right. Yeah. Transformers are, were kind of a new thing. I can't remember exactly when they came out, maybe 2018, something like that, right from Google. They were an architecture that showed that you didn't really need to have a recurrent neural network in order to deal with language. So that was one of the earlier things, you know, and Google translate and other language processing systems, people were using recurrent neural networks, networks that sort of had feedback from one time step to the next. But now we have the transformers, which instead use what they call an attention mechanism where the entire text that the system is dealing with is available all at once. And the name of the paper, in fact was Attention is All You need. And that by attention is all you need they meant this particular attention mechanism in the neural network, and that was really a revolution and enabled this new era of large language models.Eric Topol (02:34):Yeah. And as you aptly pointed out, that was in, that was five years ago. And then it took like, oh, five years for it to become in the public domain of Chat GPT. So what was going on in the background?Melanie Mitchell (02:49):Well, you know, the idea of language models (LLMs) that is neural network language models that learn by trying to predict the next word in a, in a text had been around for a long time. You know, we now have GPT-4, which is what's underlying at least some of ChatGPT, but there was GPT-1 and GPT-2, you probably remember that. And all of this was going on over those many years. And I think that those of us in the field have seen more of a progression with the increase in abilities of these increasingly large, large language models. that has really been an evolution. But I think the general public didn't have access to them and ChatGPT was the first one that like, was generally available, and that's why it sort of seemed to appear out of nothing.SPARKS OF ARTIFICIAL GENERAL INTELLIGENCESentience vs IntelligenceEric Topol (03:50):Alright. So it was kind of the, the inside world of the computer science kinda saw a more natural progression, but people were not knowing that LLMs were on the move. They  were kinda stunned that, oh, look at these conversations I can have and how, how humanoid it seemed. Yeah. And you'll recall there was a fairly well-publicized event where a Google employee back I think last fall was, put on suspension, ultimately left Google because he felt that the AI was sentient. Maybe you'd want to comment that because that's kind of a precursor to some of the other things we're going to discuss,Melanie Mitchell (04:35):Right? So yeah, so one of the engineers who was working with their version of ChatGPT, which I think at the time was called LaMDA was having conversations with it and came to the conclusion that it was sentient, whatever that means, , you know, that, that it was aware that it had feelings that it experienced emotions and all of that. He was so worried about this and he wanted, you know, I think he made it public by releasing some transcripts of his conversations with it. And I don't think he was allowed to do that under his Google contract, and that was the issue.  tThat made a lot of news and Google pushed back and said, no, no, of course it's not sentient. and then there was a lot of debate in the philosophy sphere of what sentient actually means, how you would know if something is sentient. And it Yeah. and it's kind of gone from there.Eric Topol (05:43):Yeah. And then what was interesting is then in March based upon GPT-4 the Microsoft Research Group published this sparks paper where they said, it seems like it has some artificial general intelligence, AGI qualities, kind of making the same claim to some extent. Right?Melanie Mitchell (06:05):Well, that's a good question. I mean, you know, intelligence is one thing, sentience is another. There's a question of whether, you know, how they're related, right? Or if they're related at all, you know, and what they all actually mean. And these terms, this is one of the problems. Of course, these terms are not well-defined, but most, I think most people in AI would say that intelligence and sentience are different. You know something can be intelligent or act intelligently without having any sort of awareness or sense of self or, you know, feelings or whatever sentience might mean. So I think that the sparks of AGI paper from Microsoft was more about this, that saying that they thought GPT-4 four, the system they were experimenting with, showed some kind of generality in its ability to deal with different kinds of tasks. You know, and this, this contrasts with the old, older fashioned ai, which typically was narrow only, could do one task, you know, could play chess, could play Go, could do speech recognition, or could, you know, generate translations. But it, they couldn't do all of those things. And now we have these language models, which seemed to have some degree of generality.The Persistent Gap Between Humans and LLMsEric Topol (07:33):Now that gets us perfectly to an important Nature feature last week which was called the “Easy Intelligence Test that AI chatbots fail.” And it made reference to an important study you did. First, I guess the term ARC --Abstract and Reasoning Corpus, I guess that was introduced a few years back by Francois Chollet. And then you did a ConceptARC test. So maybe you can tell us about this, because that seemed to have a pretty substantial gap between humans and GPT-4.Melanie Mitchell (08:16):Right? So, so, so Francois Chollet is a researcher at Google who put together this set of sort of intelligence test like puzzles visual reasoning puzzles that tested for abstraction abilities or analogy abilities. And he put it out there as a challenge. A whole bunch of people participated in a competition to get AI programs to solve the problems, and none of them were very successful. And so what, what our group did was we thought that, that the original challenge was fantastic, but the prob one of the problems was it was too hard, it was even hard for people. And also it didn't really systematically explore concepts, whether a, a system understood a particular concept. So, as an example, think about, you know, the concept of two things being the same, or two things being different. Okay?(09:25):So I can show you two things and say, are these the same or are they different? Well, it turns out that's actually a very subtle question. 'cause when we, you know, when we say the same we, we can mean sort of the, the same the same size, the same shape, the same color, this, you know, and there's all kinds of attributes in which things can be the same. And so what our system did was it took concepts like same versus different. And it tried to create lots of different challenges, puzzles that had that required understanding of that concept. So these are very basic spatial and semantic concepts that were similar to the ones that Solei had proposed, but much more systematic. 'cause you know, this is one of the big issues in evaluating AI systems is that people evaluate them on particular problems.(10:24):For example, you know, I think a lot of people know that ChatGPT was able to answer many questions from the bar exam. But if you take like a single question from the bar exam and think about what concept it's testing, it may be that ChatGPT could answer that particular question, but it can't answer variations that has the same concept. So we tried to take inside of this arc domain abstraction and reasoning corpus domain, look at particular concepts and say, systematically can the system understand different variations of the same concept? And then we tested this, these problems on humans. We tested them on the programs that were designed to solve the ARC challenges, and we tested them on G P T four, and we found that humans way outperformed all the machines. But there's a caveat, though, is that these are visual puzzles, and we're giving them to GPT-4, which is a language model, a text, right? Right. System. Now, GPT four has been trained on images, but we're not using the system that can deal with images. 'cause that hasn't been released yet. So we're giving the system our problems in a text-based format rather than like, like giving it to humans who actually can see the pictures. So this, this can make a difference. I would say our, our our, our results are, are preliminary .Eric Topol (11:57):Well, what do you think will happen when you can use in inputs with images? Do you think that it will equilibrate there'll be parity, or there still will be a gap in that particular measure of intelligence?Melanie Mitchell (12:11):I would predict there, there will still be a big gap. Mm-hmm. , but, you know, I guess we'll seeThe Biggest Question: Stochastic Parrot or LLM Real Advance in Machine Intelligence?Eric Topol (12:17):Well, that, that's what we want to get into more. We want to drill down on the biggest question of large language models. and that is, are they really you know, what is their level of intelligence? Is it something that is beyond the so-called stochastic parrot or the statistical ability to adjudicate language and words? So there was a paper this week in Nature Human Behavior, not a journal that normally publishes these kind of papers. And as you know it was by Taylor Webb and colleagues at U C L A. And it was basically saying for analogic reasoning ,making analogs, which would be more of a language task,  I guess, but also some image capabilities that it could do as well or better than humans. And these were college students. So , just to qualify, they're, they're not, maybe not, they're not fully representative of the species, but they're at least some learned folks. So what did, what did you think of that study?Melanie Mitchell (13:20):Yeah, I found it really fascinating. and, and kind of provocative. And, you know, it, it kind of goes along with a, a many, there's been many studies that have, have been applying tests that were kind of designed for humans, psychological tests to large language models. And this one was applying sort of analogy tests that, that psychologists have done on humans to, to, to large language models. But there's always kind of an issue of interpreting the results because we know these large language models most likely do not think like we do. Hmm. And so one question is like, how are they performing these analogies? How are they making these analogies? So this brings up some issues with evaluation. When we try to evaluate large language models using tests that were designed for humans. One question is, were these tests at all actually in the training data of a large language model? Like, had they, you know, these language models are trained on enormous amounts of text that humans have produced. And some of the tests that that paper was using were things that had been published in the psychology literature.(14:41):So one question is, you know, to what extent were those in this training data? It's hard to tell because we don't know what the training data exactly is. So that's one question. Another question is are these systems actually using analog reasoning the way that we humans use it? Or are they using some other way of solving the problems? Hmm. And that's also hard to tell. 'cause these systems are black boxes, but it might actually matter because it might affect how well they're able to generalize. You know, if I can make an analogy usually you would assume that I could actually use that analogy to understand some new, you know, some new situation by an analogy to some old situation. But it's not totally clear that these systems are able to do that in any general way. And so, you know, I tdo hink these results, like these analogy results, are really provocative and interesting.(15:48):But they will require a lot of further study to really make sense of what they mean, like to when you give, when, when the, the, you know, ChatGPT passes a bar exam, you might ask, well, and let's say it's, you know, it does better than most humans, can you say, well, can it now be a lawyer? Can it go out and replace human lawyers? I mean, a human who passed the bar exam can do that. But I don't know if you can make the same assumption for a language model, because it's the way that it's doing, answering the questions in a way that its reasoning might be quite different and not imply the same kinds of more general abilities.Eric Topol (16:32):Yeah. That's really vital. And something else that you just brought up in multiple dimensions is the problem of transparency. So we don't even know the, the specs, the actual training, you know, so many of the components that led to the model. and so you, by not knowing this we're kind of stuck to try to interpret it. And I, I guess if you could comment about transparency seems to be a really big issue, and then how are we going to ever understand when there's certain aspects or components of intelligence where, you know, there does appear to be something that's surprising, something that you wouldn't have anticipated, and how could that be? Or on the other hand, you know, why is it failing? so what is, is transparency the key to this? Or is there something more to be unraveled?Melanie Mitchell (17:29):I think transparency is, is a big part of it. Transparency, meaning, you know, knowing what data, the system was trained on, what the architecture of the system is. you know, what other aspects that go into designing the system. Those are important for us to understand, like how, how these systems are actually work and to assess them. There are some methods that people are using to try and kind of tease out the extent to which these systems have actually developed sort of the kind of intelligence that people have. So, so one, there was a paper that came out also last week, I think from a group at MIT where they looked at several tasks that were given that GPT-4 did very well on that seemed like certain computer programming, code generation, mathematics some other tasks.(18:42):And they said, well, if a human was able to generate these kinds of things to do these kinds of tasks, some small change in the task probably shouldn't matter. The human would still be able to do it. So as an example in programming, you know, generating code, so there's this notion that like an array is indexed from zero. The first number is, is indexed as zero, the second number is indexed as one, and so on. So but some programming languages start at one instead of zero. So what if you just said, now change to starting at one? Probably a human programmer could adapt to that very quickly, but they found that GPT-4 was not able to adapt very well.Melanie Mitchell (19:33):So the question was, is it using, being able to write the program by sort of picking things that it has already seen in its training data much more? Or is it able to, or is it actually developing some kind of human-like, understanding of the program? And they were finding that to some extent it was more the former than the latter.Eric Topol (19:57):So when you process all this you lean more towards because of the pre-training and the stochastic parrot side, or do you think there is this enhanced human understanding that we're seeing a level of machine intelligence, not broad intelligence, but at least some parts of what we would consider intelligence that we've never seen before? Where do you find yourself?Melanie Mitchell (20:23):Yeah, I think I'm, I'm, I'm sort of in the center ,Eric Topol (20:27):Okay. That's good.Melanie Mitchell (20:28):Everybody has to describe themselves as a centrist, right. I don't think these systems are, you know, stochastic parrots. They're, they're not just sort of parroting the data that they, they've been trained on, although they do that sometimes, you know, but I do think there is some reasoning ability there. Mm-hmm. , there is some, you know, what you might call intelligence. You know, it's, it's, but the, the question is how do you characterize it and, and how do you, I for the most important thing is, you know, how do you decide that it, that these systems have a general enough understanding to trust them,Eric Topol (21:15):Right? Right. You know,Melanie Mitchell (21:18):You know, in your field, in, in medicine, I think that's a super important question. They can, maybe they can outperform radiologists on some kind of diagnostic task, but the question is, you know, is that because they understand the data like radiologists do or even better, and will therefore in the future be much more trustworthy? Or are they doing something completely different? That means that they're going to make some very unhuman like mistakes. Yeah. And I think we just don't know.End of the Turing TestEric Topol (21:50):Well, that's, that's an important admission, if you will. That is, we don't know. And as you're, again I think really zooming in on, on for medical applications some of them, of course, are not so critical for accuracy because you, for example, if you have a, a conversation in a clinic and that's made into a note and all the other downstream tasks, you still can go right to the transcript and see exactly if there was a potential miscue. But if you're talking about making a diagnosis in a complex patient that can be, if, if you, if we see hallucination, confabulation or whatever your favorite word is to characterize the false outputs, that's a big issue. But I, I actually really love your Professor of Complexity title because if there's anything complex this, this would fulfill it. And also, would you say it's time to stop talking about the Turing tests that retire? It? It's, it's over with the Turing test because it's so much more complex than that .Melanie Mitchell (22:55):Yeah. I mean, one problem with the Turing test is there never was a Turing test. Turing never really gave the details of how this, this test should work. Right? And so we've had Turing tests with chatbots, you know, since the two thousands where people have been fooled. It's not that hard to fool people into thinking that they're talking to a human. So I, I do think that the Turing test is not adequate for the, the question of like, are these things thinking? Are they robustly intelligent?Eric Topol (23:33):Yeah. One of my favorite stories you told in your book was about Hans Clever and the you know, basically faking out the potent that, that there was this machine intelligence with that. And yeah, I, I think this, this is so apropo a term that is used a lot that a lot of people I don't think fully understand is zero shot or one shot, or can you just help explain that to the non-computer science community?Melanie Mitchell (24:01):Yeah. So, so in the context of large language models, what that means is so I could, so do I give you zero, zero shot means I just ask you a question and expect you to answer it. One shot means I give you an example of a question and an answer, and now I ask you a new question that you, you should answer. But you already had an example, you know, two shot is you give two examples. So it's just a ma matter of like, how many examples am I going to give you in order for you to get the idea of what I'm asking?Eric Topol (24:41):Well, and in a sense, if you were pre-trained unknowingly, it might not be zero shot. That is, if, if the, if the model was pre-trained with all the stuff that was really loaded into that first question or prompt, it might not really qualify as a zero shot in a way. Right?Melanie Mitchell (24:59):Yeah. Right. If it's already seen that, if it's learned, I think we're getting, it's seen that in its training data.The Great LLM (Doomsday?) Debate: An Existential ThreatEric Topol (25:06):Right. Exactly. Now, another topic that is related to all this is that you participated in what I would say is a historic debate. you and Yann LeCun, who I would not have necessarily put together . I don't know that Yan is a centrist. I would say he's more, you know, on one end of the spectrum versus Max Tegmark and Yoshua BengioEric Topol (25:37):Youshua Bengio, who was one of the three notables for a Turing award with Geoffrey Hinton So you were in this debate. I think called a Musk debate.Melanie Mitchell (25:52):Monk debate. Monk.Eric Topol (25:54):Monk. I was gonna say not right. Monk debate. Yeah. the Monk Debates, which is a classic debate series out of, I think, University of TorontoMelanie Mitchell (26:03):That's rightEric Topol (26:03):And it was debating, you know, is it all over ? Is AI gonna, and obviously there's been a lot of this in recent weeks, months since ChatGPT surfaced. So can you kind of give us, I, I tried to access that debate, but since I'm not a member or subscriber, I couldn't watch it, and I'd love to actually but can you give us the skinny of what was discussed and your position there?Melanie Mitchell (26:29):Yeah. So, so actually you can't, you can access it on YouTube.Eric Topol (26:32):Oh, good. Okay. Good. I'll put the link in for this. Okay, great.Melanie Mitchell (26:37):Yeah. so, so the, the resolution was, you know, is AI an existential threat? Okay. By an existential, meaning human extinction. So pretty dramatic, right? and there's been, this debate actually has been going on for a long time, you know, since, since the beginning of the talks about this, the “singularity”, right? and there's many people in the sort of AI world who fear that AI, once it becomes quote unquote smarter than people will be we'll lose control of it.(27:33):We'll, we'll give it some task like, you know, solve, solve the problem of carbon emissions, and it will then misinterpret or mis sort of not, not care about the consequences. it will just sort of maniacally try and achieve that goal, and in, in the process of that, for accidentally kill us all. So that's one of the scenarios. There's many different scenarios for this, you know and the, you know, debate. The debate was, it was very a debate is kind of an artificial, weird structured discussion where you have rebuttals and try, you know. But I think the debate really was about sort of should we right now be focusing our attention on what's called existential risk, that is that, you know, some future AI is going to become smarter than humans and then somehow destroy us, or should we be more focused on more immediate risks, the ones that we have right now like AI creating disinformation, fooling people and into thinking it's a human, magnifying biases in society, all the risks that people, you know, are experiencing immediately, right. You know, or will be very soon. and that the debate was more about sort of what should be the focusEric Topol (29:12):Hmm.Melanie Mitchell (29:13):And whether we can focus on very shorter, shorter immediate risks also, and also focus on very long-term speculative risks, and sort of what is the likelihood of those speculative risks and how would we, you know, even estimate that. So that was kind of the topic of the debate. SoEric Topol (29:35):Did, did you all wind up agreeing then thatMelanie Mitchell (29:38):? No. Were youEric Topol (29:38):Scared or, or where, where did it land?Melanie Mitchell (29:41):Well, I don't know. Interestingly what they do is they take a vote at the beginning of the audience. Mm-hmm. And they say like, you know, how many people agree with, with the resolution, and 67 percent of people agreed that AI was an existential threat. So it was two thirds, and then at the end, they also take a vote and say like, how many, what percent of minds were changed? And that's the side that wins. But ironically, the, the voting mechanism broke at the end, . So technology, you know, for the win ,Eric Topol (30:18):Because it wasn't a post-debate vote?Melanie Mitchell (30:21):But they did do an email survey. Oh. Oh. Which is I think not very, you know,Eric Topol (30:26):No, not very good. No, you can't compare that. No.Melanie Mitchell (30:28):Yeah. So I, you know, technically our side won. Okay. But I don't take it as a win, actually. ,Are Your Afraid? Are You Scared?Eric Topol (30:38):Well, I guess another way to put it. Are you, are you afraid? Are you scared?Melanie Mitchell (30:44):So I, I'm not scared of like super intelligent AI getting out of control and destroying humanity, right? I think there's a lot of reasons why that's extremely unlikely.Eric Topol (31:00):Right.Melanie Mitchell (31:01):But I am, I do fear a lot of things about ai, you know, some of the things I mentioned yes, I think are real threats, you know, real dire threats to democracy.Eric Topol (31:15):Absolutely.Melanie Mitchell (31:15):That to our information ecosystem, how much we can trust the information that we have. And also just, you know, to people losing jobs to ai, I've already seen that happening, right. And the sort of disruption to our whole economic system. So I am worried about those things.What About Open-Source LLMs, Like Meta’s Llama2?Eric Topol (31:37):Yeah. No, I think the inability to determine whether something's true or fake in so many different spheres is putting us in a lot of jeopardy, highly vulnerable, but perhaps not the broad existential threat of the species. Yeah. But serious stuff for sure. Now another thing that's just been of interest of late is the willingness for at least one of these companies Meta to put out their model as an open Llama2. Two I guess to, to make it open for everyone so that they can do whatever specialized fine tuning and whatnot. Is that a good thing? Is that, is that a, is that a game changer for the field? Because obviously the computer resources, which we understand, for example, GPUs [graphic processing units] used-- over 25,000 for GPT-4, not many groups or entities have that many GPUs on hand to do the base models. But is having an open model, like Meta’s available is that good? Or is that potentially going to be a problem?Melanie Mitchell (32:55):Yeah, I think probably I would say yes to both .Eric Topol (32:59):Okay. Okay.Melanie Mitchell (33:01):No, 'cause it is a mixed bag. I, I think ultimately, you know, we talked about transparency and open source models are transparent. I mean, I, I don't know if, I don't think they actually have released information on the data they use to train it, right? Right. So that, it lacks that transparency. But at least, you know, if you are doing research and trying to understand how this model works, you have access to a lot of the model. You know, it would be nice to know more about the data it was trained on, but so there's a lot of, there's a lot of big positives there. and it also means that the data that you then use to continue training it or fine tuning it, is not then being given to a big company. Like, you're not doing it through some closed API, like you do for open AI(33:58):On the other hand, these, as we just saw, talked about, these models can be used for a lot of negative things like, you know, spreading disinformation and so on. Right. And giving, sort of making them generally available and tuneable by anyone presents that risk. Yeah. So I think there's, you know, there's an analogy I think, you know, with like genetics for example, you know, or disease research where I think there was a, the scientists had sequenced the genome of the smallpox virus, right? And there was like a big debate over should they publish that. Because it could be used to like create a new smallpox, right? But on the other hand, it also could be used to, to, to develop better vaccines and better treatments and so on. And so I think there, there are, you know, any technology like that, there's always the sort of balance between transparency and making it open and keeping it closed. And then the question is, who gets to control it?The Next Phase of LLMs and the Plateau of Human-Derived Input ContentEric Topol (35:11):Yeah. Who gets to control it? And to understand the potential for nefarious use cases. yeah. The worst case scenario. Sure. Well, you know, I look to you Melanie, as a leading light because you are so balanced and, you know, you don't, the interest thing about you is what I have the highest level of respect, and that's why I like to read anything you write or where you're making comments about other people's work. Are you going write another book?Melanie Mitchell (35:44):Yeah, I'm thinking about it now. I mean, I think kind of a follow up to my book, which as you mentioned, like your book, it was before large language models came on the scene and before transformers and all of that stuff. And I think that there really is a need for some non-technical explanation of all of this. But of course, you know, every time you write a book about AI, it becomes obsolete by the time it's published.Eric Topol (36:11):That that's I worry about, you know? And that was actually going be my last question to you, which is, you know, where are we headed? Like, whatever, GPT-5 and on and it's going, it's the velocity's so high. it, where can you get a steady state to write about and try to, you know, pull it all together? Or, or are we just going be in some crazed zone here for some time where the things are moving too fast to try to be able to get your arms around it?Melanie Mitchell (36:43):Yeah, I mean, I don't know. I, I think there's a question of like-- can AI keep moving so fast? You know, we've obviously it's moved extremely fast in the last few years and, but the way that it's moved fast is by having huge amounts of training data and scaling up these models. But the problem now is it's almost like the field is run out of training data generated by people. And if people start using language models all the time for generating text, the internet is going be full of generated text, right? Right. HumanEric Topol (37:24):WrittenMelanie Mitchell (37:24):Text. And it's been shown that if these models keep, are sort of trained on the text that they generate themselves, they start behaving very poorly. So that's a question. It's like, where's the new data going to come from?Eric Topol (37:39):, and there's lots of upsettedness among people whose data are being used.Melanie Mitchell (37:44):Oh, sure.Eric Topol (37:45): understandably. And as you get to is there a limit of, you know, there's only so many Wikipedias and Internets and hundreds of thousands of books and whatnot to put in that are of human source content. So do we reach a, a plateau of human derived inputs? That's really fascinating question. I perhaps things will not continue at such a crazed pace so we can I mean, the way you put together A Guide for Thinking Humans was so prototypic because it, it was so thoughtful and it brought along those of us who were not trained in computer science to really understand where the state of the field was and where deep neural networks were. We need another one of those. And you're no one, I nominate you to help us to give us the, the, the right perspective. So Melanie, Professor Mitchell, I'm so grateful to you, all of us who follow your work remain indebted for keeping it straight. You know, you don't get ever get carried away. and we learn from that, all of us. It's really important. 'cause this, you know, there's so many people on one end of the spectrum here, whether it's doomsday or whether this is just stochastic parrot or open source and whatnot. It's really good to have you as a reference anchor to help us along.Melanie Mitchell (39:13):Well, thanks so much, Eric. That's really kind of you. Get full access to Ground Truths at erictopol.substack.com/subscribe

Transcript with some hyperlinksEric Topol (00:00):Hello, Eric Topol here. And what a privilege to have as my guest Al Gore, as we discuss things that are considered existential threats. And that includes not just climate change but also recently the concern about A.I. No one has done more on the planet to bring to the fore the concerns about climate change. And many people think that the 2006 film, An Inconvenient Truth, was the beginning, but it goes way back into the 1980s. So, Al it's really great to have you put in perspective. Here we are with the what's going on in Canada with more than 12 million acres of forest fires that are obviously affecting us greatly, no less the surface temperature of the oceans. And so many other signs of this climate change that you had warned us about decades ago are now accelerating. So maybe we could start off out, where are we with climate change and the climate reality?The Good News on Climate ChangeAl Gore (01:00):Oh, well, first of all, thank you so much for inviting me to be on your podcast again, Eric. It's always a pleasure and especially because you're the host and we, we have very interesting conversations that aren't on the podcast. So, , I'm looking forward to this one. So, to start with climate you know, the old cliche, there's good news and bad news. Unfortunately, there's an abundance of bad news but there's also an awful lot of good news. Let me start with that first and then turn to the more worrying trends. We have seen the passage in the US last August of the largest and most effective best funded climate legislation passed by any nation in all of history. The so-called Inflation Reduction Act is an extraordinary piece of legislation.(01:55): It's billed as allocating $369 billion to climate solutions. But actually, the heavy lifting in that legislation is done by tax credits, most of which are open-ended and uncapped, and a few without any time limits, most a 10-year duration. And the enthusiastic response to the legislation after President Biden signed it has now made it clear that that early estimate of 369 billion is a low-ball estimate, because Goldman Sachs, for example, is predicting that it will end up allocating 1.2 trillion to climate solutions. A lot of other investors and others using economic models are estimating more than a trillion. So, it's really a fantastic piece of legislation and other nations are beginning to react and respond and copy it. One month after that law was passed the voters of Australia threw out their climate denying government and replaced it with a climate-friendly government, which immediately then set about passing legislation that adopts the same goals as the US IRA and the Australian context.(03:19):And they stopped the biggest new coal mine there. And anyway, one month after that, in October, the voters of Brazil threw out their former president often called the “Trump of the Tropics” and replaced him with a new president, a former president who's a new president, who has pledged to protect the Amazon and the European Union in responding to the evil, evil and cruel invasion of Ukraine by Russia. And the attempted blackmail of nations in Europe, dependent on Russian gas and oil responded not by bending their knee to Vladimir Putin, but by saying, wait a minute, this makes renewable energy, freedom, energy. And so they accelerated their transition. And so these are all excellent signs and qualifies as good news. The other good news is not all that new, but it's still continuing to improve.(04:28):And that is the astonishing reductions in cost for electricity produced by solar and wind, and the reductions in cost for energy storage, principally in batteries and electric vehicles and a hundred other less well known technologies that are extremely important. We're in the midst of early stages of a sustainability revolution that has the magnitude of the industrial revolution, coupled with the speed of the digital revolution. And we're seeing it all over the place. It’s really quite heartening. One quick example last, the, the biggest single source of global warming pollution is the generation of electricity with gas and coal. Well, last year, if you look at all the new electricity generation capacity installed worldwide 90% of it was renewable. In India, 93% was solar and wind. And India's pledged not to give permits for any new coal burning plants for at least five years, which means never, probably because this cost reduction curve, as I mentioned, is still continuing downward electric vehicles, we're now seeing that the purchases have reached 15% of the market globally.(05:56):Norway's already at 50%. They've actually outlawed the sale of any new internal combustion engines. And indeed, many national and even municipal and state jurisdictions have prospectively served notice that they, you won't be able to buy them after a certain day, 2030,  in many cases and the auto companies and truck and bus companies have long since diverted their research money all their R & D is going into EVs now. And that's the second largest source of global warming pollution. I could go through the others, but I want, I'll just tell you that there is a lot of good news.And the Bad NewsNow, the bad news is we're still seeing the crisis get worse, faster than we're deploying all of these solutions. And, the inertia in our political and economic systems is partly a direct result of huge amounts of lobbying and campaign contributions and the century old net of political and economic influence built up by the fossil fuel industry.(07:18):And they're opposing every single solution at the state level, the local level, the national level, the international level. Now, this COP 28 [the 2023 United Nations Climate Change Conference] coming up at the end of the year in the United Arab Emirates is actually chaired by an oil and gas company CEO-- It's preposterous. And they already have in the last two COPS, more lobbyists registered as participants than all than the five or six largest national delegations combined. And we're seeing them really oppose this change. And meanwhile, the manifestations of the crisis are steadily worsening. You mentioned the fires in Canada that are predicted to burn all summer long. And I was in New York City last week, and you, you know, from the news stories it, it was horrific. I got there the day after the worst day, oh my God.(08:21):But I saw and heard from people just the tremendous problems that people have. It's also going on in Siberia, by the way, and these places that are typically beyond the reach of TV crews and networks that don't capture our attention unless something happens to blow the smoke to where we live. And that's what's happened here. But there are many other extremely worrying manifestations that aren't getting much attention. I do think we're going to solve this, Eric. I'm very optimistic, but the question is whether we will solve it in time. We are what's the right way to say this? We're tiptoeing through a minefield with tripwires and toward the edge of a cliff. I don't want to torture the metaphor, but actually there are several extremely dangerous threats to ecological systems that are in a state of balance now, and are being pushed out of their equilibrium state into a different format.(09:35):The ocean currents--we're already seeing it with the jet stream in the northern hemisphere. You may have seen on the weather maps. They're now using these a lot where it's getting loopier and more disorganized. That's what the last few winners has, has pulled these big loops, have pulled arctic air down into areas far south in the US and in other regions, by the way. And it’s making a lot of the extreme events worse. Now, we're entering an El Nino phase in the Pacific Ocean comes around every so often, and this one is predicted to be a strong one, and that's going to accentuate the temperature increase. You know, it was [recently] 110 degrees last week in Puerto Rico, 111 degrees in several countries in Southeast Asia.(10:31):Last summer, China had a heat wave that the historians say about, which the historians say there's nothing even minimally comparable in all prior known, and the length, the extent, the duration, the intensity. And we saw monsoons lead to much of Pakistan underwater for an extended period of time. I could go on, but the net and balance out the good news and the bad news we are gaining momentum. And soon we are going to be gaining on the crisis itself and start deploying solutions faster than it's getting worse. So I remain optimistic, and I always remind people, if you doubt we have the political will to see this through, remember that political will is itself a renewable resource.The Intersection of A.I. and Climate ChangeEric Topol (11:27):Yeah, that's a great optimistic point, and we sure appreciate that, because it's pretty scary to see these trends that you reviewed. Now, as you know recently there was a large group of AI scientists this one led by Sam Altman of OpenAI, who put out a statement, a one-sentence statement, and it said, “Mitigating the risk of distinction from ai, which you and are enthusiastic about, should be a global priority alongside other societal scale risks, such as pandemics and nuclear war.” Well, obviously, also climate change. So how do you see the AI intersection of climate change? Because as you well know, GPT-4, having pre-trained with some 30,000 graphic processing units [GPUs], the issues about consumption of energy carbon emissions, the need for water cooling, is AI going to make this situation worse, or will it make it better?Al Gore (12:33):Well, yeah. You know, I understand. Well, both would be my answer. And we don't have enough data yet to really know for sure which way it will tip. Maybe we'll talk about the existential risks from generative AI. As this conversation continues, there are many who have spoken up and said, well, wait a minute, before we focus on that, we need to look at the risks that are right, staring us right in the face. I mean, the use of these AI driven algorithms, not necessarily generative AI, but the AI-driven algorithms in social media are causing tremendous harm right now. You've heard about the rabbit holes that people get drawn down into on the internet. That's because of the AI-driven algorithms and the tracking of confidential information about what people are looking at and what they're interested in.(13:40):And these are rabbit holes are ,a little bit not to shift metaphors, a little bit like pitcher plants in that they have slippery slides and, oh, and, you know, what's at the bottom of the rabbit hole? That's where the echo chamber is. And when you spend long enough in the echo chamber, then those who are feeding the information to you weaponize a new form of AI, not artificial intelligence, artificial insanity. And, and we see it all over the place where people are utterly convinced of completely ridiculous and provably false conclusions and, and motivated to go out and act in the real world. On that basis we, we see the fakes and the concerns about video and audio deep fakes, and how that's going to have an impact on us and, and all manner of other concerns that need to need to be addressed.(14:43):But the existential threat is one that I do want to come back to. But, turning to your specific focus on whether it is going help or hurt or both where climate is concerned, I have co-founded a coalition called Climate TRACE that uses AI in an extremely effective, beneficial way. Trace stands for tracking real-time atmosphere, carbon emissions, and we have a coalition of AI firms, NGOs, university groups and the whole coalition works together to identify with AI, the point source of every single significant stream of emissions of global warm inclusion everywhere on the planet. We released it at the last United Nations Conference, the one that was held in Egypt last year. The top 72,000 emission point sources around the world this fall; we will release the top 70 million emission sources.(15:54): We also have every agricultural field in the world down to a 10 meter by 10 meter resolution. We have all, every single power plant, all the steel mills, every large ship, every large plane, most every well, we have all of the significant greenhouse gas emissions that wouldn't have not, that would not have been possible without ai. Now, this is not generative AI. We have used generative ai --not ChatGPT--we tried that, but there are others that are actually more proficient in the views of our team members at writing code. It has saved us time and enhanced our productivity in writing code. So that's one example where AI has been a big help. And we see it in modeling, and we see it in the preparation for adaptation and in other ways. Now, the downside is, you said in your introductory phrasing that the energy requirements and the emissions are just enormous because it is an extremely energy intensive exercise.(17:09): And you have to have the GPUs as well as the energy. So it's you could call it “oligopogenic”-- that may not be a word. It may be a hallucination, like GPT is famous for, but what I mean is it, it does tend to favor a very small number, a very wealthy, very powerful, very large companies. Basically, Google and Microsoft are driving the, the rest of the world to try to desperately catch up. You know, the CEO of Microsoft. They stole a march on Google with the release of ChatGPT and then that fascinated people and the pickup and use of GPT unbelievable is just, it, it's there's been nothing like it in.(18:19):Previous technological history. The CEO said that he wanted to make call Google out and make him dance. Well you know, Peggy Noonan said in one of her columns, that's not a responsible way for the CEO of such a company to talk. I, I like him, and I'm not really taking a poke at him, insofar as I'm making the point that there're really two companies, and the internal dynamic between the two is driving this frenzy of investment and activity, and the underlying platform, the large language models, they're all almost a commodity now. They're all over the place and have been for a while. But the need for the GPUs, the need for the energy consumption that's limiting the cutting edge developments to these two companies. For now, China doesn't trust it because they don't trust the enhanced political influence.(19:22):It might give those using it or the enhanced insight. And there are others that will try to find a way to use it, of course. But the, the emissions itself are extremely harmful and the use of generative AI in the hands of irresponsible actors. And, unfortunately, we're human beings and we have a lot of irresponsible actors around this, around this country, around the world. And they could use that to really put climate disinformation into high gear. They, they can use it in a variety of ways to further enhance the disruption, the disruptive tactics they've used in the past.Eric Topol (20:15):Yeah. Well, that's what I wanted to get into more on this. We have, I think, you know, if you want to put an existential risk at the highest level, maybe if you were assign 10 to climate change and you've brought up the fact that the large language models generative AI will make worse, the things we've already seen, the, the hacking of democracy and all the fake stuff that's the conspiracy theories that it will reinforce. And the question is, where are you, where did you place the whole generative AI era that we've now entered in if you were to weigh it against existential threat, just other, one other thing. You've, you undoubtedly, because you read more than anyone I know you're a true scholar, and you've read these doomsayer essays about hacking a democracy and(21:11): the end of the world, and some of the notable leaders in AI like Geoffrey Hinton to leave Google. And so we have, on the one hand some people saying this is a real threat to the world. And then we have Marc Andreesen who wrote, “Why AI Will Save the World” last week , a long read on this. So where do you, where do you see the existential threat of now that AI has gone into high gear, as you noted, more than a billion unique users of ChatGPT within 90 days, which is unprecedented. I mean, withAl Gore (21:45):All cap, nothing else is even close in history. Yeah,Where are we with Artificial General Intelligence?Eric Topol (21:48):Yeah. So, do you see that this has been exaggerated, the risk of generative AI? Or how do you compare it to the climate change crisis?Al Gore (22:01):Well it's a great question, Eric. And of course lots of people we know are breaking their brains trying to answer that question. I think we need a little more experience with it because our understanding is going to develop as we have more experience. But at the same time, we're trying to catch up in our basic understanding of what the heck's going on with these things. And they don't actually know it's important to note they don't know how it's doing what it's doing. And I'll, I'll circle back to that. But while we're trying to figure it out, it's continuing to advance at warp speed. GPT-4 in the cleverly titled, the provocatively titled, research paper “Sparks of Artificial General Intelligence” that Microsoft put out is already demonstrating capacities that are shockingly comparable to human capacity is the way they put it.(23:13):This less than a year after Google fired a young researcher named Blake Lemoine who said that he thought theirs had become sentient. And they fired him right away. These multiple co-authors of this paper from Microsoft weren't fired. They're in charge of the thing, and they're basically saying close to what the guy at Google said, who got fired.I think that if you listen to Geoffrey Hinton, the so-called godfather of generative AI, and there's so many, many parents of generative AI. But what caused him to change his mind, in his words, were when he realized that it is very likely to become much smarter than we are, than the smartest human beings ever are. And coupling that level of superintelligence, the phrase some have used with access to all of the knowledge that humanity has ever compiled means there is an unpredictable unquantifiable risk that we might no longer be the apex lifeform on this planet.(24:47):And that generative AI might be used that in ways that would be threatening to us. I think we need more experience with it in before we decide, okay, that's it. We not going to unplug all these dang things and bust them up with sledgehammers. That's not going to happen. Cause there's so many different entities pursuing it. But, you know, I placed this the context of one of the themes in that runs through the history of science, Eric. And that is, as we have seen in the past, new discoveries that have challenged our human understanding of our place in creation. For example, when Galileo said, the Earth's not the center of the universe, it's not the center of the solar system, the church said ah, off to prison with you, they put him on trial.(25:58):because that challenge our prime place in what we had thought was God's design. Then Darwin, of course, placed us solidly in the animal kingdom, descended from, from primates and apes and monkeys. And of course, that struggle is still, I used to represent Dayton, Tennessee and the United States House of Representatives where, where the, the Scopes Trial took place, the so-called monkey trial. And there have been a succession of other similar blows to the collective ego of humanity. We used to assume confidently that the earth was probably the only place in the whole universe that life where life emerged. And now the common assumption is it's ubiquitous throughout the universe and maybe in advanced forms and lots and lots of places. And by the way, the universe isn't the only universe they tell us.(26:55):Now, the emerging better view is that we're in a multiverse, and that's all above my pay grade. But within that, within that continuum of successive blows to the collective ego of humanity, here comes an assertion that something other than a human being may be conscious. And our immediate reaction, as it, as our predecessors' reactions were with Galileo and Darwin, et cetera, nah, that can't be we're special. No, it can't be. We're the only ones. Well maybe not. They are edging closer and closer to a point where scientists and engineers are likely to say, yep, it is conscious. Maybe it won't happen. I kind of think it is already beginning to happen. I think there's an explanation for it, but we're going to have to catch up to that explanation. And we're going have to build this airplane of regulation and safeguards while we taxi it out to the runway.Can AI Help Solve the Climate Crisis?Eric Topol (28:06):Well, you know, I share that view. You know, I don't think that continuing to say this is just a stochastic parrot is where we're at right now. It's a form of intelligence from machines that we haven't seen previously. And as you've really zoomed in on this is the big debate about the level of understanding the so-called “world model.” And, you know, this is something that is only going to get more capable over time. And that gets me to kind of close the loop on our discussion. Do you foresee that we could get to a point where our machine help would come up with new solutions? I mean, as you've summarized, you have phenomenal AI tracking of climate change, but could you foresee that there are potential solutions that we haven't thought of, that, that generative AI could help us as humans to solve the climate crisis?Al Gore (29:05):Yeah, I think that's very likely.  You know, one of the new professions that's just emerged as a, a prompt engineer—we'll have to have people trained in prompting these large language models in a way that gets us to the kinds of exchanges you're talking about. But we've, even before generative AI arrived, we have had multiple examples of artificial intelligence solving problems that we humans have not been able to solve. One example that I wrote about several years ago was the long-term effort to try to decode the genetics of a little thing called the planarian worm. It's been of extreme interest because it can regenerate every part of its body. And in, in such an efficient way they've been trying to understand it.(30:07):So a group of scientists took all of the raw data from all of the failed experiments collected during all of the failed experiments to try to solve that problem, fed 'em into an AI. And the AI said, okay, here's the answer. And it was credited. The AI agent was credited as one of the co-authors of the resulting study. We've had we've had problems in fluid dynamics solved by artificial intelligence that were impenetrable to us. So there's no question in my mind that some of the solutions that we're looking for, for the climate crisis will be found with the assistance of generative AI. I'm certain of that.Eric Topol (30:53):Well, that adds to the optimism that we want to close up with because we need that in the face of what we're seeing that's palpable every day regarding climate change. And, you know, I think this discussion, Al,  I could spend the whole day with you because it's so stimulating and your ability to cite history, as well as current and future perspective is, for me, unparalleled. So, I really enjoyed this discussion with you, and I hope we'll have another one real soon, because this generative AI era is zooming, like I've never seen ChatGPT in November, GPT-4 in March, and you know what's next here.Al Gore (31:35):So GPT-5 is coming in December, as you said. And, before you conclude, Eric, let, let me just give back to you my admiration for the work that you've been doing on the applications of generative AI in healthcare and the development of even better healthcare technologies. You're the leading exponent of this whole field of knowledge now. And you know, you helped us get through the, our effort to understand the pandemic and all the twists and turns and all of that. And now you're taking the lead on the application of AI in healthcare, and thank you very much. I speak for a lot of people in saying that.Eric Topol(32:19):Well, that's really kind to you. That's, that's where my interest was before the pandemic. And now the good part is to be able to get back to it full force. But I do think, unlike the overall existential concerns regarding AI and the large language models of AI, the net benefit for healthcare is just much more obvious. Yes, there are concerns, of course, regarding patient prompts and getting inaccurate responses. However, what it can do for the, the medical community and for patient autonomy is, is really quite extraordinary. So, in that regard another good way to, to sum up our, our discussion here because that's a very, I'm very sanguine about, as we get better about implementing AI in healthcare, it'll make a big difference particularly now with this multimodal AI that brings in images, the records, you all the data that voice, you know, the ambient voice of office visits, as well as even bedside rounds. It's really quite exciting. And I know we're going be talking about that some more in the months ahead. So thank you so much. You've, you've brightened up this day because all I keep seeing are these apocalyptic photos of New York and what's going on out there, graphs of the oceans sea surface temperature. And I'm thinking, oh my, how we keep losing ground on what you told us about for decades. And I like hearing that you think these solutions are and be increasingly to catch up to that. So thank you.Al Gore (33:59):Thank you, Eric. Get full access to Ground Truths at erictopol.substack.com/subscribe

TRANSCRIPTEric Topol (00:00):Hello, this is Eric Topol, and it's really a delight for me to welcome Hannah Davis who was the primary author of our recent review on Long Covid and is a co-founder of the Patient-Led Research Collaborative. And we're going to get into some really important topics about citizen science, Long Covid and related matters. So, Hannah, welcome.Hannah Davis (00:27):Thank you so much for having me.Eric Topol (00:29):Well, Hannah, before we get into it I thought because you had a very interesting background before you got into the patient led research collaborative organization with graphics and AI and data science. Maybe you could tell us a bit about that.Hannah Davis (00:45):Sure. Yeah. Before I got sick, I was working in machine learning with a particular focus on generative models for art and music. so I did some projects like translating data sets of landscapes into emotional landscapes. I did a project called The Laughing Room, where there was a room and you went in and the room would listen to you and laugh if it thought you said something funny, . and then I did a lot of generative music based on sentiment. So I, I did a big project where I was generating music from the sentiment of novels and a lot of kind of like critical projects, looking at biases in data sets, and also curating data sets to create desired outcomes in these generative models.Eric Topol (01:30):So, I mean, in a way again, you were ahead of your time because that was before ChatGPT in November last year, and you were ahead of the generative AI curve. And here again, you're way ahead in in the citizen science era as it particularly relates to the pandemic. So, I, I wonder if you could just tell us a bit I think it was back, we go back to March, 2020. Is that when you were hit with Covid?Hannah Davis (01:59):Yes.Eric Topol (02:00):And when did you realize that it wasn't just an acute phase illness?Hannah Davis (02:06): for me, honestly, I was not worried at all. I, my first symptom was that I couldn't parse a text message. I just couldn't read it, thought I was tired. an hour later, took my temperature, realized I had a fever, so that's when I kind of knew I was sick. but I really just truly believed the narrative I was going to get better. I was 32 at the time. I had no pre-existing conditions. I just was, you know, laying around doing music stuff, not concerned at all. And I put a calendar note to donate plasma two weeks out, and I was like, you know, I'm going to hit that mark. I'm going to donate plasma, contribute, it'll be fine. And that day came and went. I was still, you know, pretty sick with a mild case. You know, I didn't have to be hospitalized.(02:49):I didn't have severe respiratory symptoms. but my neurological symptoms were substantial and did increase kind of over time. And so I, I was getting concerned. Three weeks went by, still wasn't better. And then I read Fiona Lowenstein’s op-ed in the New York Times. They were also very young. They were 26 at the time, they had been hospitalized, and they had this prolonged recovery, which we now know as Long Covid. and they started the Body Politic Support Group joined that saw thousands of people with the same kind of debilitating brain fog, the same complete executive functioning loss, inability to drive, forgetting your family members' names who were all extremely young, who all had mild cases. and that's kind of when I got concerned because I realized, you know, this was not just happening to me. This was happening to so many people, and no one understood what was happening.Eric Topol (03:49):Right. extraordinary. And, and was a precursor, foreshadowing of what was to come. Now, here it is, well over three years later. And you're still affected by all this, right?Hannah Davis (04:02):Yes. Pretty severely.Eric Topol (04:04):Yeah. And I learned about that when I had the chance to work with you on the review. You were the main driver of this review, and I remember asking you, because I, I didn't know anyone in the world that was tracking Long Covid like you and to be the primary author. And then you sent this outline, and I had never seen an outline in all my years in academic medicine. I never saw an outline like this of the review. I said, oh my God, this is incredible. So I know that during that time when we worked on the review together, along with Lisa McCorkell and Julia Moore Vogel, that, you know, there, there were times when you couldn't work on it right there, there were just absolutely, you would have some good days or bad days. And, and that's the kind of, is that kind of the way is, how it goes in any given unit time?Hannah Davis (04:55):I think generally, I, I communicated as like 40% of my function is gone. So, like, I used to be able to have very, very full days, 12 hour days would work, would socialize, would do music, whatever. you know, I, I have solidly four functional hours a day. on a good day, maybe that will be six. On a bad day, that's zero. And when I push myself by accident, I can get into a crash that can be three to seven days easily. Hmm. and then I'm, then I'm just not, you know, able to be present. I don't feel here. I don't feel cognitively able, I can't drive. And then I'm just completely out of the world for a bit of time.Eric Topol (05:35):Yeah. Wow. So back in the early days of when you were first got sick and realized that this was not going to just go away, you worked with others to form this Patient -Led Research Collaborative organization, and here you are, you didn't have a medical background. You certainly had a data science and computing backgrounds. But what were your thoughts? I mean, citizen science has taken on more of a life in recent years, certainly in the last decade. And here there's a group of you that are kind of been leading the charge. we'll get to, you know, working with RECOVER and NIH in just a moment. But what were your thoughts as to whether this could have an impact at working with these, the other co-founders?Hannah Davis (06:27):I think at first we really didn't realize how much of an impact we were going to have. The reason we started collecting data in the first place really was to get answers for ourselves as patients. You know, we saw all these kind of anecdotes happening in the support group. We wanted to get a sense of which were happening the most at what frequency, et cetera. and it really wasn't until after that when like the CDC and WHO started reaching out, asking for that data, which was gray literature at the time that we kind of realized we needed to formalize this and, and put out an official paper which was what ended up being the second paper. But the group that we formed really is magical, I think like, because the primary motivator to join the group was being sick and wanting to understand what was happening. And because everyone in the group only has the kind of shared experience of, of living with Long Covid, we ended up with a very, very diverse group. Many, many different and I think that really contributed to our success in both creating this data, but also communicating and, and doing actionable policy and advocacy work with it.Eric Topol (07:42):Did you know the folks before? Or did you all come together because of digital synapses?Hannah Davis (07:47):Digital synapses? I love that. Absolutely. No, we didn't know each other at all. they're now all, you know, they're my best friends by far. you know, we've been through this, this huge thing together. but no, we didn't meet in person until just last September, actually. And many of them we still haven't even met in person. which makes it even more magical to me.Eric Topol (08:13):Well, that's actually pretty extraordinary. So together you've built a formidable force to stand up for the millions and millions of people. As you wrote in the review, 65 million people around the world who are suffering in one way or another from Long Covid. So just to comment about the review --you know, I've been working in writing papers for too long, 35 years. I've never, in my entire career, over 1300 peer reviewed papers on varied topics, ever had one that's already had 900,000 downloads, is the fourth most cited paper and Altmetric since published the same timeframe in January of all 500,000 peer-reviewed papers. Did you ever think that the, the work that, that you did and our, you know, along with Lisa and doing, and I would ever have this type of level of interest?Hannah Davis (09:16):No, and honestly, it's so encouraging. Our, our second paper to me did very well. and, you know, was, was widely viewed and widely cited, and this one just surpassed that by miles. And I think that it's encouraging because it communicates that, that people are interested, right? People, even if they don't understand what long covid is, there is a huge desire to know. And I think that putting this out in this form, focusing on the biomedical side of things really gives people a, a tool to start to understand it. And from the patient side of things, more than any other paper I've heard we, we get so many comments that are like, oh, I brought this to my doctor and, you know, the course of my care change. Like he believed me and he started X treatment. and that, that's the kind of stuff that just makes us so, so meaningful. and I'm so, so grateful that, that we were able to do this.Eric Topol (10:16):Yeah. And as you aptly put it, you know, a work of love, and it was not easy because the reviewers were not not all of them were supportive about the real impact, the profound impact of long covid. So when you now every day you're keeping track of what's going on in this field, and there's something every single day. one of the things, of course is that we haven't really seen a validated treatment all this time, and you've put together a list of candidates, of course, it was in the review, and it constantly gets revised. What are some of the things that you think are alluring from preliminary data or mechanisms that might be the greatest unmet need right now of, of getting some relief, some remedy for this? What, what, what's your sense about that?Hannah Davis (11:13):I think the one I'm most excited about right now are JAK/STAT inhibitors. And this is because one of the leading researchers in viral onset illness Ron Davis and that group believe that basically they're, they have a shunt hypothesis, and that means they, they basically think there's a switch that happens in the body after you've, you've had a viral illness like this, and that that switch can actually be unswitched. And that, to me, as a patient, that's very exciting because, you know, that that's what I imagine a cure kind of looks like. and they did some computational modeling and, and identified JAK/STAT inhibitors as one of the promising candidates. so that's from like the, like hypothetical side that needs to be tested. And then from the patient community, from some things we're seeing I think really easily accessed ones include chromolyn sodium.(12:14):So these are prescription antihistamines. they're both systemic. So Coen has been seeming to work for patients with brain fog and sleep disorders. And chromolyn sodium particularly works in, in patients with gastrointestinal mast cell issues. People are going on to kind of address the micro clots. I, for me personally, has been one of the biggest changers game changers for my brain fog and kind of cognitive impairment type things. but there's so many others. I mean, I think we, we really wanna see trials of anticoagulants. I'm personally really excited to start on ivabradine which is next up in my queue. And, and seems to have been a, a game changer for a lot of patients too. I V I G has worked for patients who are, have been able to get it, I think for both I V I G and ivabradine. Those are medications that are challenging to get covered by insurance. And so we're seeing a lot of those difficulties in, in access with a couple of these meds. But yeah, just part of, part of the battle, I guess,Eric Topol (13:32):You know, one of the leading of many mechanisms that in this mosaic of long covid is the persistence of virus or virus components. And there have been at least some attempts to get some Paxlovid trials going. Do you see any hope for just dealing, trying to inactivate the virus as  a way forward?Hannah Davis (13:54):Absolutely. Definitely believe in the viral persistence theory. I think not only Paxlovid, but other a covid antivirals. I know that Steve deas and Michael Paluso at U C S F are starting a couple long covid trials with other covid antivirals that yeah, for sure. I think they all obviously need to be trialed A S A P. And then I also think on the viral persistence lens, ev like almost everyone I know has viral reactivation of some sort like EBV, CMV,  VZV, you know, we obviously see a lot of chickenpox or shingles reactivations and antivirals targeting those as well I think are really important.Eric Topol (14:41):Yeah. Well, and I also, just the way you're coming out with a lot of this, you know terminology and, you know science stuff like I V I G for intravenous immunoglobulin and for those who are not, you know, just remember, this is a non-life science expert who now has become one. And that goes back again to the review, which was this hybrid of people who had long covid with me who didn't to try to come up with the right kind of balance as to, you know, what synthesizing what, what we know. And I think this is something the medical profession has never truly understood, is getting people who are actually affected and, and becoming, you know, the real experts. I mean, I, I look to you as one of the world's leading authorities, and I learn from you all the time.(15:35):So that goes to RECOVER. So there was a long delay in the US to recognize the importance of long covid. Even the UK was talking to patients well before they ever had a meeting here in the us, but eventually, somehow or other they allocated a billion dollars towards long covid research at the NIH. And originally, you know, fortunately Francis Collins, when he was director, saw the importance, and he, I learned bequeathed that 2 of the NIH institutes, one of the directors, Gary Gibbons visited me recently because of a negative comment I made about RECOVER. But before I go over my comment, you've been as he said, you, and Lisa McCorkell ,among others from the Patient-led Collaborative have had a seat at the table. That's a quote from Gary. Can you tell us your impression about RECOVER you know, in terms of at least they are including Patient-Led research folks with long covid as to are they taking your input seriously? And what about the billion dollars ?Hannah Davis (16:46):Oh, boy. tricky question. I don't even know where to start. Well, I mean, so I think recover really messed up by not putting experts in the field in charge, right? Like we are, we have from the beginning have needed to do medical provider education at the same time that all these studies started getting underway. And that was just a massive amount of work to try to include the right test to convince medical professionals why they weren't necessary. all that could have been avoided by putting the right people in charge. And unfortunately, that didn't happen. unfortunately recovers our, our best hope still or at least the, the best funded hope. so I really want to see it succeed. I think that they, they have a long way to go in terms of, of really understanding why patient representation matters and, and patient engagement matters.(17:51):I, you know, it's been a couple years. It's, it's still very hard to do engagement with them. it's kind of a gamble when you get placed on a, a committee if they are going to respect you or not. And, and that's kind of hard as people Yeah. Who are experts now, you know, I've been in the field of Long Covid research more than anyone really I'm working with there. I, I really hope that they improve the research process, improve the publication process. the, a lot of the engagement right now is, is just tokenization. you know, they, they have patient reps that are kind of like just a couple of the patient reps are kind of yes men you know, they, they get put on higher kind of positions and things like that. but they're, I think there's 57 patient reps in total spread across committees. we don't have a good organizing structure. We don't know who each other are. We don't really talk to each other. there, there's room for a lot of improvement, I would say, well,Eric Topol (18:59):The way I would put it is, you know, you kind of remember it like when you have gatherings where there's an adult table, and then there's the kiddie's table. Absolutely. Folks are at the kiddy table. I mean, yeah. And it's really unfortunate. So they had their first kind of major publication last week, and it's led to all sorts of confusion. you wrote about it, what did we, what did we glean from that, from that paper that was reported as a 10% of people with covid go on to Long Covid, and there were clearly a risk with reinfections. Can you kind of review that and also what have we seen with respect to the different strains as we go on from, from the Wuhan ancestral all the way through to the  various lineages of omicron. Has that led to differences in what we've seen with Long Covid?Hannah Davis (19:56):Yeah, that's a great question and one that I think a lot of people ask just because it, you know, speaks to the impact of long covid on our future. I think not just this paper, but many other papers at this point, also, the, the ONS data have shown that that Long Covid after omicron is, is very common. I think the last ONS data that came out showed of everyone living with Long Covid in the UK. After Omicron, which was the highest group of all of them. we certainly saw that in the support groups also, just, just so many people. but people are still getting it. I think it's because it, most cases of Long Covid happen after a mild infection, 75 to 90%. And when you get covid, now, it is a mild infection, but whatever the pathophysiology is, it doesn't require severe infection.(20:50):And you know, where I think we hopefully have seen decreases in like the, the pulmonary and the cardiovascular like organ damage types we're not seeing real improvements at all in kind of the long term and the neurological and the ones that end up lasting, you know, for years. And that's really disappointing. in terms of the paper, you know, I think there were two parts of the paper. There were those, those items you mentioned, which I think are really meaningful, right? The, the fact that re infections have a higher rate of long covid is like ha needs to have a substantial impact on how we treat Covid going forward. that one in 10 people get it after Omicron is something we've been, you know, shouting for, for over a year now. and I think this is the first time that will be taken seriously.(21:42): but at the same time, the way RECOVER communicated about this paper and the way that you talked to the press about this paper shows how little they understand the post-viral history right, of, of like thinking about a definition.  Why wouldn't they know that would upset patients? You know, that and the fact that they, in my opinion you know, let patients take the brunt of that anger and upset you know, where they should have been at the forefront, they should have been engaging with the patient community on Twitter is really upsetting as well. Yeah.Eric Topol (22:20):Yeah. And you know, I, when I did sit down with Gary Gibbons recently, and he was in a way wanting to listen about how could recover fulfill its goals. And I said, well, firstly, you got to communicate and you got to take the people very seriously not just as I say, put 'em at the Kiddie table, but, you know, and then really importantly is why isn't there a clinical trial testing any treatment? Still today, not even a single trial has been mounted. There's been some that have been, you know, kind of in the design phase, but still not for the billion dollars. All that's been done is, is basically following people with symptoms as already had been done for years previously. So it's, it's just so vexing to see this waste and basically confusion that's been the main product of RECOVER to date and exemplified by this paper, which is apparently going to go through some correction phases and stuff. I mean, I don't know, but whether that's going to the two institutes that it's, it's N H L B I, the National heart, lung and Blood, and the Neurologic Institute, NINDS, that are the two now in charge of making sure that RECOVER recovers from where it's, it's at right now. And yeah, so lack of treatments, and then the first intervention study that was launched incredibly was exercise. Can you comment about that?Hannah Davis (23:56):It's unreal. You know, it's, it, it just speaks to the lack of understanding the existing research that's in this space. Exercise is not a treatment for people with hem. It has made people bedbound for life. The risks is are not, the risks are substantial. that there was no discussion about it, that there was no understanding about it. That, you know, even patients who don't have pem who wouldn't necessarily be harmed by this trial deserve better, right? They still deserve a trial on anticoagulants or literally anything else than exercise. And there's, it just, it, it's extremely frustrating to see it, it would have been so much better if it was led by people who already had the space, who didn't have to be educated in post exertional malaise and the, the underlying underpinnings of it. and just had a sense of, of how to continue forward and, you know, patients deserve better.(24:55):And I think we're, we're really struggling because yeah, there's, there's going to be five trials as I understand it, and that's not enough. And none of them should be behavioral or lifestyle interventions at all. you know, I think it also communicates just the, the not understanding how severe this is. And I get that it's hard. I get that when you see patients on the screen, you think that they're fine and that's just how they must look all the time. But recover doesn't understand that for every hour they're asking patients to engage in something that's an hour, they're in bed, you know, that, that they're, they take so much time away from patients without really understanding like the, the minimum they should be able to do is, is understand the scope and the severity of the condition, and that we need to be trialing substantially more serious me treatments than, than exercise. right,Eric Topol (25:54):Right, right. And also the recognition, of course, as you know very well about the subtypes of long covid. So, you know, for example, the postural orthostatic tachycardia syndrome pots and how, you know, there's a device, so you don't have to always think about drugs where you put it in the back of your ear and it's neuromodulator to turn down your vagus nerve and not have the dizziness and rapid heart rate when you stand and all the other symptoms. And, you know, it costs like a dollar to make this thing. And why don't you do a trial with that? I mean, that was one of the things, it doesn't have to always be drugs, and it doesn't have to, it certainly shouldn't be exercise. But you know, maybe at some point this will get on on track. Although I'm worried that so much of the billion dollars has already been spent and no less the loss of time here, I people are suffering. Now, that gets me to this lack of respect lack of every single day we are confronted with people who don't even believe there's such a thing as long covid after all this time, after all these people who've had their lives profoundly disrupted.(27:04):What, what can you say about this?Hannah Davis (27:07):It's just a staggering, staggering lack of empathy. And I think it's also fear and a defense mechanism, right? People want to believe that they have more control over their lives than they do, and they want to believe that, that it's not possible for them personally to get a virus and then never recover and have their life changed so substantially. I really genuinely believe the people who don't believe long covid is real at this point you know, have their own things going on. And just, yeah,Eric Topol (27:38):It's kinda like how Covid was a hoax, and now this is, I mean, the, you, you just, ofHannah Davis (27:44):Course, but it's true, like it's happened with, it happened with me, CF s it happened with HIV AIDS. Mm-hmm. someone just showed me a brochure of, of a 10 week lifestyle exercise intervention for aids, you know, saying that you could positively think your way out of it. All that is, is, is defense mechanism, just, yeah. You know, it's repeating the same history over and over.Eric Topol (28:07):Well, I think you nailed it. And of course, you know, it was perhaps easier with Myalgic encephalomyelitis when it weren't as many people affected as the tens of millions here, but to be in denial. the other thing is the young people perfectly healthy that are those who are the most commonly affected. a lot of the people who I know who have been hit are like you, you know, very young and, and you know like Julia in my group who, you know, was a big runner and, you know, can't even go blocks at times without being breathless. And this is the typical, I mean, I saw in clinic just yesterday, an older fellow who had been in the hospital for a few weeks and has terrible long covid. And yes, the severity of covid can correlate with the sequela, but because of just numbers, most people are more your phenotype. Right, Hannah.Hannah Davis (29:08):Right, exactly. It's a weird like math thing for people to wrap their head around. Like, yes, if you're hospitalized, the chance of getting long covid is much, much higher than if you were not hospitalized. But because the vast number of cases were not hospitalized, the vast number of long cases, long covid cases were not hospitalized. but I think like all of these things are interesting clues into the pathophysiology. You know, we also see people who were hospitalized who recover faster than some of these, the neurocognitive mild, my mild encephalomyelitis subtypes for sure. I think all of that is, is really interesting and can point to clues about kind of what is, what is happening at the core.Eric Topol (29:54):Yeah. And that I wanted to get into before I wrap up some of the things that are new or added since our review in published in January. so I just recently reviewed the brain in long covid with these two German studies, one of which showed the spike protein was lighting up in the reservoir, the kind of initial reservoir, the brain, the skull, and the meninges. the, the, basically the layers covering the brain, the, particularly the skull bone marrow. And that's where all these immune cells are in high density that are patrolling the brain. And so it really implicated spike protein per se, in people who've had covid. and then the other German study, which was so striking in mild covid, the majority of people where they had it 10 months later, all this signature by m r i, quantitative, m r i of major inflammation with free water and this so-called mean diffusivity, which is basically the leaking and you know, the inflammation of the brain.(31:01):And so, and that's as long as they follow the people, you know, if they followed 'em three years, they'd probably still see this. And so there's a lot of brain inflammation that is linked to the symptoms as you've described. You know, the brain fog, the memory executive function. But we have no remedy. We have no way, how can we stop the process? How can we turn it around like, as you mentioned, like a jak stat inhibitor in other ways that we desperately need to get into testing. so that was one thing I, I wonder, I mean, I think people who have had the symptoms of cognitive effects know there's something going wrong in their brain, but here is, you know, kind of living proof that what there's sensing is now you can see it. thoughts about that?Hannah Davis (31:52):I mean, I think the research is just staggering. It's so, so validating as someone, you know, who was living this and living the severity of it, you know, without research for years, it's, it's wonderful to finally see so many things come out. but it's overwhelming research. And I, I don't understand kind of the lack of urgency. Those are two huge, huge studies with huge implications. you know, that the, that the spike would still be in the skull like that in the, in the bone marrow like that. and the neuroinflammation I think, you know, feels very obvious in terms of what, like the symptoms end up presenting. why aren't we trialing things like the, the, this is just destroying people's lives. Even if you don't care about people's lives, like it will destroy the economy. Like people are still getting this, this is not decreasing. these are really, really substantial tangible injuries that are happening.Eric Topol (32:52):Yeah, I know. And, and there's not enough respect for preventing this. The only way we know to prevented it for sure is just not to get covid, of course. Right. And then, you know, things like vaccines help to some extent. The magnitude, we don't know for sure, you know, maybe metformin helps but, you know, prevention and everyone's guard, not everyone, but you know, vast majority, you know, really let down at this point when there's not as much circulating virus as there has been. Now, another area where it has really been lit up since our review was autoimmune diseases. So we know there's this common link in some people with long covid. There's lots of auto antibodies and self-destruction that's ongoing. The immune system has gone haywire. But now we've learned, you know, this much higher incidence of rheumatoid arthritis and lupus and across, you know, every one of the autoimmune diseases.(33:44):So the impact besides the brain autoimmune diseases and then the one that just blows me away at the beginning of the pandemic, even in the first year there were starting to see more people showing up with type two diabetes and say, ah, well it must be a coincidence. And now there are 12 large studies, every single one goes through of a significant increase in type two diabetes and, and possibly even autoimmune diabetes, which makes sense. So this is the thing I wanted to clarify cuz a lot of people get mixed up about this, Hannah, there's the symptoms of long covid, some of which we reviewed, many of the long lists we haven't. But then there's also the sequela to organ hits like the diabetes and immune system and the brain and you know, also obviously kidney and heart and on and on. Can you help differentiate? Cause a lot of people get mixed up by all this stuff.Hannah Davis (34:46):Yeah, I mean I think, you know, we started out with symptoms because that's what we knew, that's what we were talking about. but I do think it's helpful to start, and I, I do think it would be helpful to do a big review on conditions and that does include ME/CFS and Diso but also includes diabetes, includes heart attacks and strokes are includes dementia risks. and yeah, I think the, the difficulty with kind of figuring out what, what percent of long covid are each of these conditions is really biased by the fact that for that, doctors can't recognize me CFS and dysautonomia that it doesn't end up in the EHR data. And so we can't really do these large scale like figuring out the percentage of what is what. but I think like, I, I saw someone describe long covid recently as like a, a large scale neurocognitive impairment emergency, a a large scale cardiovascular event emergency. I think those are extremely accurate. the immune system dysfunction is really severe. I really would like to see the conversation start moving more toward the, the conditions and the pathophysiologies based on what we're finding yeah, more than, more than just the symptoms.Eric Topol (36:15):Right. And then, you know, there's this other aspect of the known unknown, so with two other viruses. So for example, back in 1918 with influenza, it, it took 15 years to see or more that this would lead to a significant increased risk of Parkinson's disease. And then with polio, the post-polio syndrome showed up up to 30 years later with profound progressive muscular atrophy and, you know, falls and all sorts of major neurologic hits that were due for from the original polio virus. And so, yeah, some of the things that we're learning here with long covid hopefully will spill over to all these other post-infectious processes. But I think what's emphasizing in our discussion is how much more we, we really do need to learn how we desperately need some treatments, how we desperately need to have the respect for this syndrome that it deserves which still isn't there, it's just, it's unfathomable to me that we still have people dissing it on a daily basis and, and not, you know, a small minority, but actually a pretty strident group that's, that's not so small.(37:35):Now, before you wrap up, what have I missed here? Hannah with you, because this is a rarefied opportunity to have a sit down with you about what's going on in long covid and also to emphasize citizen science here because this is, if there's anything I've ever seen in my career to show the importance of citizen science, it's been the long covid story. you as one of the leaders of it. So have I missed something?Hannah Davis (38:05):I feel like we actually covered a pretty good bit. I would say maybe just for people listening, emphasizing that long covid is still happening. I think, you know, so many people that we see recently got long covid after getting vaccinated or having a prior infection and just kind of relaxing all their precautions and they're, they're angry. You know, the, the newer group of long Covid folks are angry because they were lied to that they were safe, and that's completely reasonable. you know, that it's still happening in, in one in 10 vaccinated omicron infections is a huge deal. and, and I think yeah, just re-emphasizing that, but overall that, yeah, you know, this is very serious. I think there's my, my MO for Twitter, really, honestly, despite all the, the accusations of fear mon mongering, I really don't put extreme stuff online, but I really do believe that this is this is currently leading to, you know, higher rates of, of heart attacks.(39:08):I do believe that we will see a, a wave of early onset dementia that is honestly is happening already you know, happening in my friend group already. and like you said there, there's a lot of unknowns that can be speculated about the fact that we see E P V reactivation in so many people. Are we gonna see a lot of onset multiple sclerosis mm-hmm. you know, lymphomas other E B V sequelae, like the danger's not over the danger's actually, like pretty solidly. there's pretty solidly evidence for some, some pretty serious things to come and you know, I keep saying we gotta get on top of it now, butEric Topol (39:55):Well, I, I always the, unfortunately, some, some people don't realize it, but the eternal optimist that we will get there, it's taking too long, but we got to ratchet up the heat, get projects like RECOVER  and elsewhere in the world to go in high gear and, you know, really get to testing the promising candidates. You so have aptly outlined here and in your writings. you know, I think this has been an incredible relationship that I've been able to develop with you and your colleagues and I've learned so much from you and I will continue to be following you. I hope everyone listening that if they don't already follow you and, and others that are trying to keep us up to speed, which you know, just this week again, there was a Swiss study, two year follow up showing that the number of people that were still affected significantly with long covid symptoms at two years was 18%.(40:58):That's a lot of folks, and they were unvaccinated, but still, I mean, they, in order to have two year follow up, you're going to see a lot of people who before the advent of vaccines. So this, if you look at the data, the research carefully and it gets better quality as time goes on, because we have control groups, we have matched controls, we have, you know, hopefully the beginning of randomized trials of treatment. we'll hopefully get some light. And part of the reason we're going to get there is because of you and others, getting us fully aware, keeping track of things, getting the research committee to be accountable and not just pass off the same old stuff, which is not really understanding the condition. I mean, how can you start to really improve it if you don't even understand it? And who are you going turn to to understand it? you don't, you don't just look at, you know, MRI brain studies or immune lab studies. You got to talk to the folks who, who know it and know it so well.. All right, well this has been hopefully one of many more conversations we'll have in the future and at some point to celebrate some progress, which is what we so desperately need. Thank you so much, Hannah.Hannah Davis (42:19):Thank you so much. Absolute pleasure.LinksOur Long Covid review with Lisa McCorkell and Julia Moore-Vogelhttps://www.nature.com/articles/s41579-022-00846-2The Brain and Long Covidhttps://erictopol.substack.com/p/the-brain-and-long-covidHeightened Risk of Autoimmune Diseaseshttps://erictopol.substack.com/p/the-heightened-risk-of-autoimmuneCovid and the Risk of Type 2 Diabeteshttps://erictopol.substack.com/p/new-diabetes-post-acute-covid-pascThanks for listening and reading Ground Truths.Please share if you found this informative.Your free subscription denotes your support of this work. Should you decide to become a paid subscriber you should know that all proceeds go to support Scripps Research. That has already helped to bring on several of our summer high school and college interns. Get full access to Ground Truths at erictopol.substack.com/subscribe

Link to the book: The AI Revolution in MedicineLink to my review of the bookLink to the Sparks of Artificial General Intelligence preprint we discussedLink to Peter’s paper on GPT-4 in NEJMTranscript (with a few highlights in bold of many parts that could be bolded!)Eric Topol (00:00):Hello, I'm Eric Topol, and I'm really delighted to have with me Peter Lee, who's the director of Microsoft Research and who is the author, along with a couple of colleagues for an incredible book called The AI Revolution in Medicine, GPT-4 and Beyond. Welcome, Peter.Peter Lee (00:20):Hello Eric. And thanks so much for having me on. This is a real honor to be here.Eric Topol (00:24):Well, I think you are in the enviable position of having spent now more than seven months looking at GPT-4’s S capability, particularly in the health and medicine space. And it was great that you recorded that in a book for everyone else to learn because you had such a nice head start.  I guess what I wanted to start with is, I mean, it's, it's a phenomenal book. I [holding the book up], this prop. I can't resistPeter Lee (00:52):Eric Topol (00:53):When, when I got it, I, I couldn't, I stayed up most of the night because I couldn't put it down. It was, it is so engrossing. But when you, when you first got your hands on this and started testing it, what were, what were your initial thoughts?Peter Lee (01:09):Yeah. I, let me first start by saying thank you for the nice words about the book, but really, so much of the credit goes to the co-authors, Carey Goldberg and Zach Kohane and Corey in particular took my overly academic writing. I suspect you have the same kind of writing style as well as Zach's pretty academic writing and helped turn it into something that would be approachable to non-computer scientists and as she put it, as much as possible as a page turner. So I'm glad that her work helped make the, the book an easy read. I,Eric Topol (01:54):I want to just say you're very humble because the first three chapters that you wrote yourself were clearly the, the best ones for me. Anyway. I don't mean to interrupt, but it, it, it is an exceptional book, really.Peter Lee (02:06):Oh thank you very much. It means a lot. Hearing that from you. You know, my own view is that the, the best writing and the best analyses and the best ideas for applications or not of this type of technology in medicine are yet to come. But you're right that I did benefit from this seven-month head start. And so, you know, I think the timing is, is very good. but I'm hoping that much better books and much better writings and ideas will come, you know, when you start with something like this, I, I suspect, Eric, you had the same thing. you start off with a lot of skepticism and I, in fact, I sort of now made light with this. I talk about the nine stages of grief that you have to go through.(02:55): I was extremely skeptical. Of course, I was very aware of GPT 2, GPT 3 and GPT 3.5. I understand, you know, what goes into those models really deeply. and so some of the claims, when I was exposed to the early development, GPT-4 just seemed outlandish and impossible. So I, I was, you know, skeptical, somewhat quietly skeptical. We've all been around the block before and, you know, we've heard lots of AI claims and I was in that state for maybe more than two weeks. And then I started to become in that two weeks annoyed, because I know that some of my colleagues like falling into what I felt was the trap of getting fooled by this technology. And then that turned into frustration and fear. I actually got angry. And one colleague who I won't name I've since had to apologize because then I into the phase of amazement because you start to encounter things that you can't explain that this thing seems to be doing that turns into joy.(04:04): I remember the exhilaration of thinking, wow, I did not think I would live long enough to see a technology like this. and then intensity, There was a period of about three days when I didn't sleep, I was just experimenting. Then you run into some limits and some areas of puzzlement and that's a phase of chagrin. And then real dangerous missteps and mistakes that this system can make that you realize might end up really hurting people. and then, you know, ChatGPT gets released and to our surprise it catches fire with people. And we learn directly through communications that some clinicians are using it in clinical settings. And that heightens the concern. And I, I can't say I'm in the ninth stage of enlightenment yet, but you do become very committed to wanting to help the medical community get up to speed and to be in a position to take ownership of the question of whether, when, and how a technology like this should be used. and that was really the motivating force behind the book. And it, it was really that journey. And that journey also has given me patience with everyone else in the world, because I realize everyone else in the world has to go through those same nine, nine stages.Eric Topol (05:35):Well, those stages that you went through are actually a great way to articulate this pluripotent technology. I mean, I think you, you touched on that chat. ChatGPT was released November 30th and within 90 days had a billion distinct users, which is beyond anything in history. And then of course, this transcended that quite a bit as you showed in the book coming out in you know, just a very short time in March. right. And I think a lot of people want access to GPT-4 because they know that there is this jump in its capabilities. But the book starts off after Sam Altman's forward, which was also nice because he said, you know, this is just an early, as you pointed out there, there's a lot more to come in the large language model space.(06:30):But the grabber to me was this futuristic, this second year medical resident who's using an app on the phone to get to the latest GPT to help manage her patient, and then all the other things that it's doing to check on her patients and do all the things that are the tasks that clinicians don't really want to do, that they need help with. And that just grabs you as to the futuristic potential, which may not be so far away. And I think then you get into the nuts and bolts, but one of the things that I think is a misnomer that you really nailed is how you say it isn't just that it generates, but it really is great at editing and analyzing. And here it's, it's called generative AI. Can you, can you expound on that? And it's unbelievable conversationalist capability.Peter Lee (07:23):Yeah. you know, the term Generative AI, I tried for a while to push back on this, but I think it's just caught on and I've given up on that. And I get it. You know, I, I think especially with ChatGPT it's of course reasonable for the public to be, you know infatuated with a thing that can write love letters, write poetry and that generative capability. and of course, you know school children writing their essays and so on this way. But as you say one thing we have discovered through a lot of experimentation is it's actually somewhat of a marginal generator of text. I would not say at all. That is, it is not as good a poet as good human poets. It's not the, you know, people have programmed GPT-4 to try to write whole novels and it can do that,(08:24):they aren't great. and it's a challenge, you know within Microsoft, our Nuance division has been integrating GPT-4 to help write clinical and encounter notes. and you can tell it's just hitting at the very limits of the capabilities in and of the intelligence of GPT-4 to be able to do that well. But one area where it really excels is in evaluating or judging or reviewing things. And we've seen that over and over again. in chapter three. You know, I have this example of its analysis of some contemporary poetry which is just stunning in its kind of insights and its use of metaphor and allegory. And but then in other situations in interactions with the New England Journal Journal of Medicine experimentations with the use of GPT-4 as an adjunct to the review process for papers it is just incredibly insightful in spotting inconsistencies missing citations to precursor studies to understanding lack of inclusivity and diversity, you know, in approach or in terminology.(09:49):And these sorts of review things end up being especially intriguing for me when we think about the whole problem of medical errors and the possibility of using GPT-4 to look over the work of doctors, of nurses of insurance, adjudicators and others, just as a second set of eyes to check for errors check for kind of missing possibilities if there's a differential diagnosis. Is there a possibility that's been something that's been missed? If there's a calculation for an IV medication administration, well, it's a calculation done correctly or not. And it's in those types of applications of GPT-4 as a reviewer, as a second set of eyes that I think I've been especially impressed with. And we try to highlight that in the book.Eric Topol (10:43):Yeah. That's one of the very illuminating things about going well beyond what are the assumed utilities in a little bit, we'll talk about the liabilities, but certainly these are functions part of that flurry potent spectrum that I think a lot of people are not aware of. One, particularly of interest in the medical space is something I had not anticipated as, you know, when I wrote a Deep Medicine chapter, “Deep Empathy,” I said, well, we got to rely totally on humans for that. But here you had examples that were quite stunning of coaching physicians by going through their communication, their note and saying, you know, you could have been more sensitive with this. You could have done this, but you, you could be more empathic. And as you know, since the book was published, there was an interesting study that compared a couple hundred questions directed to physicians and then to ChatGPT, which of course isn't necessarily called, we wouldn't say it's state of the art at this point, right. But what was seen that chatbot exhibited, the more empathy, the more sensitive, higher quality responses. So do you think, ultimately that this will be a way we can actually use technology to foster a better communication between clinicians and patients?Peter Lee (12:10):Well I'll try to answer that, but then I want to turn the question to you because I'm just dying to understand how others especially leading thinkers like you think about this. Because as a human being and as a patient, there's something about this that doesn't quite sit right. You know I, I want the empathy to come from my doctor, my human doctor that's in my heart the way that I feel. And yet there's just no getting around the fact that GPT-4 and even weaker versions like GPT 3.5, CHatGPT can be remarkably empathetic. And as you say, there was that study that came out of UC, San Diego Medicine, Johns Hopkins Medicine that you know, was just another fairly significant piece of evidence to that point.Here's another example. You know, my colleague Greg Moore was assisting a patient who had late stage pancreatic cancer.(13:10):And there was a real struggle for both the specialists and for Greg to know what to say to this desperate patient how to support this patient. And the thing that was remarkable Greg decided to use GPT-4 to get advice and they had a conversation and there was very detailed advice to Greg on what to say and how to support this patient. And at the end when Greg said, thank you, GPT-4 said, and you're welcome, Greg, but what about you? You know, do you have all the support that you need? This must be very difficult for you. So the empathy just goes remarkably deep. And, you know, if you just look at how busy good doctors and especially nurses are, you can start to realize that people don't necessarily have the time to think about that.(14:02):And also that what GPT-4 is suggesting ends up being a prompt to the human doctor or the human nurse to actually take the time to reflect on what the patient might need to hear, right. What might be going through their minds. And, and so there is some empathy aid going on here. At the same time, I think as a society, we have to understand how comfortable we are with the idea of this concept of empathetic care being assisted by a machine. and this is something that I'm very keen and curious about just in the medical community. And, and that's why I wanted to turn the question back around to you. how do you see this?Eric Topol (14:46):Yeah, I didn't foresee this, but I, and I also recognize that we're talking about a machine vector of it. I mean, it's a pseudo-empathy of sort. But the fact that it can process where it can be improved and it can help foster essentially are features that I think are extraordinary. I, I wouldn't have predicted that. And I've seen now, you know, many good examples in the book and, and even beyond. So it's a welcome thing and it adds another capability which is partly isn't that, that physicians and nurses are lacking empathy, but because their biggest issue, I think is lacking time. Yes. And the fact that someday there's a rescue in the works, hopefully, that a lot of that time of tasks that are, you know, the data clerk functions and other burdens right, will be alleviated the keyboard liberation that has been a fantasy of mine for some years, maybe ultimately will be achieved.(15:52):And the other thing I think that's really special in the book that I wanted to comment, there is a chapter by I think Carey Goldberg. And that was about the patient side, right? And this is what we, we all, the talk is about, you know, doctors and clinicians, but it's the patients who could derive the most. And out of those first billion people that used ChatGPT, many were of course health and medical question conversations. But these are patients, we're  all patients. And the idea that you could have a personal  health advisor, a concept which was developed in that chapter, and the whole idea that that as opposed to a search today, that you could get citations and it would be at the, at the literacy level of the person asking them, making the prompts. Yeah. Could you comment about that? Because that seems to be very much underemphasized, this democratization of this high level capability of getting you know, very useful information and conversation.Peter Lee (16:56):Yeah. And I think also this is also where some of the most difficult societal and regulatory questions might come, because while the medical community knows how to abide by regulations, and there is a regulatory framework, the same is much less true for a doctor in your pocket, which is what GPT-4 and, you know, other large language models that are emerging can, can become. And you know, I think for me personally I have come to depend on GPT-4. I use it through the Bing search engine. sometimes it's simple things that previously weren't mysterious. Like I received an explanation of benefits notice from my insurance company, and it is this notice it has some dollar figures in it. It has some CPT codes, and I have no idea. And sometimes it's things that my son or my wife got treated for.(17:55):It's, it's just mysterious. It's great to have an AI that can decode these things and can answer questions. similarly, when I go for a medical checkup and I get my blood test results just decoding those CBC lab test numbers, it, it's, again, something that is just incredible convenience. But then even more you know, my father recently passed away. He was 90 years old, but he was very ill for the last year or so of his life seeing various specialists. I, my two sisters and I all lived far away from him. And so we were struggling to take care of him and to understand his medical care. and it's a situation that I found all too common in our world right now. And it actually creates stress and phrase of relationships amongst siblings and so on.(18:56):And so just having an AI that can take all of the data from the three different specialists and, you know, have it all summed up and be able to answer questions, be able to summarize and communicate efficiently from one specialist to the next to really provide kind of some sound advice ends up being a godsend. Not so much for my father's health, because he was on a trajectory that was really not going to be changed, but just for the peace of mind and the relationships between me and my two sisters and my mother-in-law. And so it's that kind of empowerment. you know, in corporate speak at Microsoft, we would say that's empowerment of a consumer, but it is truly empowerment. I mean, it's for real. And you know, that kind of use of these technologies, I think is spreading very, very rapidly and I think is is incredibly empowering.(19:57):Now the big question is can the medical community really harness that kind of empowered patient? I think there's a desire to do that. That's always been one of the big dreams, I think in medicine today. and then the other question is, the assistants are fallible. They make mistakes. and so, you know, what is the regulatory or legal or, you know, ethical disposition of that? And so these are still big questions I think we have to answer. But the, you know, overall big picture is that there's an incredible potential to empower patients with a, a new tool and also to kind of democratize access to really expert medical information. and I, I just think it's, you're absolutely right. It doesn't get enough attention even in our book we only devoted one chapter to this, right?Eric Topol (21:00):Right. But at Least it was in there though. That's good. At least you had it because I think it's so critical to figure that out. And as you say, the ability to discriminate bad information, confabulation hallucination among people without medical training is, is, is much more challenging. Yes. but I also liked in the book how you could go to go back to another conversation to audit the first one or a third one, so that if you ever are suspicious that you might not be getting the best information you could do, like double data entry or triple data entry, you know, I thought that was really interesting. Now Microsoft made a humongous investment in open AI yesterday Sam Altman was getting grilled, not again, not really in a much more friendly sense, I'm sure about what should we do. We have this, we have this two edge sword likes of which we've never seen.(21:59):Of course, you get in the book about does it really matter if it's AGI or some advanced intelligence? If it's working well, it's kind of like the explainability-- black box story. But of course, it, it can get off the tracks. We know that. And there isn't that much difference perhaps between ChatGPT and GPT-4 established so far. So in that discussion, he said, well, we got to have regulatory oversight and licensing. And it's very complex. I mean, what, what are your thoughts as to how to deal with the potential limitations that are still there that may be difficult to eradicate that are the worries?Peter Lee (22:43):Right. You know, at, at, at least when it comes to medicine and healthcare. I personally can't imagine that this should not be regulated. it, it just and it just seems also more approachable to think about regulation because the whole practice of medicine has grown up in this regulated space. if there's any part of life and of our society that knows how to deal with regulation and can actually make regulations actually work it is medicine. And so now having said that I do understand coming from Microsoft, and even more so for Sam Altman coming from open eye, it can sometimes be interpreted as being self-serving. You're wanting to set up regulatory barriers against others. I would say in Sam Almond's defense that at back to 2019 prior, just prior to the release of GPT-2 Sam Altman made public calls for thinking about regulation for need for external audit and, you know, for the world to prepare for the possibility of AI technologies that would be approaching AGI..(24:05): and in fact just a month before the release of GPT-4, he made a very public call saying even at greater length, asking for the for the world to, to do the same things. And so I think one thing that's misunderstood about Sam is that he's been saying the same thing for years. It isn't new. And so I think that that should give people who are suspicious of Sam's motives in calling for regulation, that it should give them pause because he basically has not changed his tune, at least going back to 2019. But if we just put that aside you know, what I hope for most of all is that the medical community, and I really look at leading thinkers like you, particularly in our best medical research institutions would quickly move to take assertive ownership of the fundamental questions of whether, when, and how a technology like this should be used would engage in the research to create the foundations for you know, for sensible regulations with an understanding that this isn't about GPT-4 this is about the next three or four or five even more powerful models.(25:31):And so, you know, ideally, I think it's going to take some real research, some real inventiveness. What we explain in chapter nine of the book is that I don't believe we have a workable regulatory framework no, right now in that we need to develop it. But the foundations for that, I think have to be a product of research and ideally research from our best thinkers in the medical research field. I think the race that we have in front of us is that regulators will rightfully feel very bad if large nervous people start to get injured or, or worse because of the lack of regulation. and so there, you know, and, and you can't blame them for wanting to intervene if that starts to happen. And so, so we do have kind of an urgency here. whereas normally our medical research on say, methods for clinical validation of large language models might take, you know, several years to really come to fruition. So there's a problem there. But at the, I think the medical field can very quickly come up with codes of contact guidelines and expectations and the education so that people can start to understand the technology as well as possible.Eric Topol (26:58):Yeah. And I think the tricky part here is that, as you know, there's a lot of doomsayers and existential threats that have been laid out by people who I respect, and I know you do as well, like Geoffrey Hinton who is concerned, but you know, let's say you have a multimodal AI like GPT-4, and you want to put in your skin rash or skin lesion to it. I mean, how can you regulate everything? And, you know, if you just go to Bing and you go to creative mode and you're going get all kinds of responses. So this is a new animal, this is a new alien, the question is that as you say, we don't have a framework and we should move to, to get one. To me, the biggest question that you, you, you really got to in the book, and I know you continue, of course, it was with within two days of your book’s publishing,  the famous preprint came out, the Sparks preprint from all your team at Microsoft Research, which is incredible.(27:54):169 page preprint downloaded. I don't how many millions of times already, but that is a rich preprint we'll, we'll put in the link, of course. But there, the question is, what are we seeing here? Is this really just a stochastic parrot a JPEG with, you know, loose stuff and juxtaposition of word linguistics, or is this a form of intelligence that we haven't seen from some machines ever before? Right. and, you get at that in so many ways, and you point out, does it matter? I I wonder if you could just expound on this, because to me, this really is the fundamental question.Peter Lee (28:42):Yeah. I think I get into that in the book in chapter three. and I think chapter three is my expression of frustration on this, because it's just a machine, right? And in that sense, yes, it is just a stochastic parrot, you know, it's a big probabilistic machine that's making guesses on the next word that it should spit out, or that you will spit out. It, it, and it's making a projection for a whole conversation. And you know, in that, the first example I use in chapter three is the analysis of this poem. And the poem talks about being splashed with cold water and feeling fever. And the machine hasn't felt any of those things And so when it's opining about those lines in the poem, it can't possibly be authentic. And so you know, so we can't say it understands these things.(29:39):It it hasn't experienced these things, but the frustration I have is as a scientist, and here's now where I have to be very disciplined to be a scientist, is the inability to prove that. Now, there has been some very, very good research by researchers who I really respect and admire. I mean, there was Josh Tenenbaum's team, whole team, and his colleagues at MIT or at Harvard, the University of Washington, and the Allen Institute, and many, many others who have just done some really remarkable research and research that's directly relevant to this question of does the large language model, quote unquote, understand what it's hearing and what it's saying? And often times providing tests that are grounded in the foundational theories about why these things can't possibly be understanding what they're saying. And therefore, these tests are designed to expose these shortcomings in large language models. But what's been frustrating is, but also kind of amazing is GPT-3tends to pass most, if not all of these tests!(31:01):And, and so it, it leaves you kind of, if we're really honest, as scientists, it and even if we know this thing, you know, is not sentient, it leaves us in this place where we're, we're without definitive proof of that. And the arguments from some of the naysayers who I also deeply respect, and I've really read so much of their work don't strike me as convincing proof either, you know, because if you say, well, here's a problem that I can use to cause GPT-4 to get tripped up, I, I have no shortage of problems. I, I think I could get you to trip, get tripped up , Eric. And yet that does not prove that you are not intelligent. And so, so I think we're left with this kind of set of two mysteries. One is we see GPT-4 doing things that we can't explain given our current understanding of how a neural transformer operates.(32:09):And then secondly we're lacking a test that's derived from theory and reason that consistently shows a limitation of GPT-4’s understanding abilities. and so in my heart, of course, I, I understand these things as machines and I actively resist anthropomorphizing these machines. But as it, I, maybe I'm fooling myself, but as a discipline scientist, I, I'm, I'm trying to stay grounded in proof and evidence. and right at the moment, I don't believe the world has that I, we'll get there. We're understanding more and more every day, but at the moment we don't have it.Eric Topol (32:55):I think hopefully everyone who's listening is getting some experience now in these large language models and realizing how much fun it is and how we're in a new era in our lives. This is a turning point.Peter Lee (33:13):Yeah. That's stage four of amazement and joyEric Topol (33:16):Yeah. No, there's no question. And you know, I think about you, Peter, because you know, at one point you were in a high level academic post at Carnegie Mellon, one of our leading computer science institutions in the country, in the world, and now you're at this enviable spot of having helped Microsoft to get engaged with a, a risk, I mean a big, big bet. And one that's fascinating, and that is obviously just an iteration for many things to come. So I wonder if you could just give us your sense about where you think we'll be headed over the next few years, because the velocity that this is moving. Not only is it this new technology that is so different than anything previously, but to go, you know, from a few months to get to where things are now and to know that this road is still a long ways in front of us. What, what's your sense of, you know, are we going to get hallucinations under control? Are we going to start to see this pluripotency rollout particularly in the health and medicine arena?Peter Lee (34:35):Yeah. You know, I think first off, I can't say enough good things about the team at OpenAI. You know, I think their dedication and their focus and I think it'll come out eventually also, the, the care that they've taken in understanding the potential risks and, and really trying to create a model for how to cope with those things. I, I think as those stories come out, I think it it will it'll be quite impressive. at the same time, it's also incredibly disruptive, even for us as researchers, it just disrupts everything. Right. You know, I was having interaction after I read Sid Muhkerjee’s's new book, the Song of the Cell. Because in that book on cellular biology one of the prime characters historically Rudolph Virchow who confirmed the cell mitosis and the you know, the thing that was disruptive about Virchow is that well, first off, the whole theory of cell mitosis was debunked.(35:44): that didn't invalidate the scientists who were working on cell mitosis, but it certainly debunks many of their scientific legacies. And the other is after Virchow, to call yourself a biology researcher, you had to have a microscope and you had to know how to use it. and in a way, there's a scientific disruption similar here, where there are now new tools and new computing infrastructure that you need, if you want to call yourself a com, a computer science researcher. And that's really incredibly disruptive. so I, I see kind of two bifurcation, I think that's likely to happen. I, I think the team at Open AI and with Microsoft's support and collaboration will continue to push the boundaries and the frontiers with the idea of seeing how close to AGI can truly be achieved and largely through scale. And you know, there, there will be tremendous focus of attention on improving its abilities in mathematics and in planning and being able to use tools and, and so on there. and in that, there's a strong suspicion and belief that as greater and greater levels of general cognitive intelligence are achieved, that issues around things like hallucination will be, become much more manageable. Or at least manageable to the same extent that they're manageable in human beings.(37:25):But then I, I think there's going to be an explosion of activity in much smaller, more specialized models as well. I think there's going be a gigantic explosion in, say, in open-source smaller models, and those models probably will not be as steerable and alignable, so they might have more uncontrollable hallucination might go off the rails more easily, but for the right applications --integrated into the right settings--that might not matter. And so exactly then how these models will get used and also what dangers they might pose, what negative consequences they might bring is hard to predict. But I, I do think we're going to see those two different flavors of these large AI systems coming really very, very quickly, much less in the next year.Eric Topol (38:23):Well, that's an interesting perspective, an important one in the book you wrote in this sentence that I thought was particularly notable “the neural network here is so large that only a handful of organizations have enough computing power to train it.” we're talking about 20 or 30,000 GPUs, something like that. We're lucky to have two here or four. this is something that I think again, if you were sitting at Carnegie Mellon right now versus sitting with at Microsoft or some of the tech titan companies that have this capabilities, can you comment about this? Because this sets off a very, you know, distinct situation we've not seen before,Peter Lee (39:08):Right?  First off you know, I can't really comment on the size of the compute infrastructure for training these things, but, but it is, as we wrote in the book, is at a size that very, very few organizations at this point. This has got to change at some point in the future. and even on the inference side, forgetting about training you know, GPT-4 is much more power hungry than the human brain. So it is just the human brain is an existence proof that there must be much more efficient architectures for accomplishing the same tasks. So I think there's really a lot yet to discover and a lot of headroom for, for improvement. but you know, what I think is ultimately the, the kind of challenge that I see here is a technology like this could become as essential infrastructure of life as the mobile phone in your pocket.Peter Lee (40:18):And, and so then the question is, can the cost of this technology, how quickly can the cost of this technology, if it should also become as necessary to modern life as the technology's in your pocket how quickly can the costs of this be get to a point where that's, you know, where that is can be reasonably accomplished, right? If we don't accomplish that, then we risk creating new digital divides that would be extremely destructive to society. And what we want to do here is to really empower everybody if it does turn out that this technology becomes as empowering as we think it could be.Eric Topol (41:04):RIght I, I think your point about the efficiency the drain on electricity and no less water for cooling. I mean, these are big, big-ticket things and, you know hopefully simulating the human brain will become, and it's less power-hungry state will become part of the future as well.Peter Lee (41:24):You, well, and hopefully these technologies will solve problems like you know, a clean energy, right? Fusion containment, all better lower energy production of fertilizers, better nanoparticles for more efficient lubricants. There's all a new catalyst for carbon capture. we, if you think about it in terms of making a bet to kind of invent our way out of climate disaster this is one of the tools that you would consider betting on.Eric Topol (42:01):Oh, absolutely. You know, I'm going to be talking soon with Al Gore about that, and I know he's quite enthusiastic about the potential. This is engrossing having this conversation, and I would like to talk to you for many hours, but I know you have to go. But I, I just want to say, as I wrote in my review of the book, talking with you is very different than talking with, you know, somebody with bravado. You're, you know, you have great humility and you're so balanced that when, when I hear something from you or read something that you've written, it's a very different perspective because I don't know anybody who's more balanced, who is more trying to say it like it is. And so, you know, I just, not everybody knows you a lot of people do that might be listening. I just want to add that and just say thank you for taking the effort, not just that you obviously wanted to experiment with GPT-4, but you also, I think, put this together in a great package so others can learn from it, and of course, expand from that as we move ahead in this new era.(43:06):So, Peter, thank you. It's really a privilege to have this conversation.Peter Lee (43:11):Oh thank you, Eric. You're really really too kind. But it, it means a lot to me to hear that from you. So thank you.Thanks for listening and or reading Ground Truths. If you found it as interesting a conversation as I did, please share it.Much appreciation to paid subscribers—you’ve already helped fund many high school and college students at our summer intern program at Scripps Research and all proceeds from Ground Truths go to Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

Transcript Eric (00:00):Okay. Hello, this is Eric Topol and this is a rare privilege for me to interview my favorite epidemiologist, Dr. Michael Osterholm. He is the Regents Professor of the University of Minnesota. He's director of CIDRAP, which is certainly one of the leading entities around the world for public health. And, we've been friends for the last few years, which we'll we'll talk about. So, welcome Michael. Such a great privilege to have you today.Michael (00:31):Well, thank you, the honor, really is mine. As I have shared with you and others know very well--you have been a real mentor to me and many others during this pandemic. And, I could never repay you adequately for all that you've helped teach me throughout these last three years. It's been immeasurable.Eric (00:49):No, if you're too kind, I think it's much different. The opposite way. I've learned so much from you because this isn't my area, as you well know. I thought we'd start with, of course, right now things are relatively good for the pandemic in the United States and mostly around the world, with relatively less cases, less hospitalizations and deaths. But obviously still people are getting infected. And maybe you can tell us about the recent case that you went through that would be enlightening.End of the Pandemic?Michael (01:28):Yeah, I think we're all trying to understand when the pandemic ends. And, as we've discussed many times before, we'll probably know that about a year after it ends, then we'll say, yep, that was the end of it. Don’t for a moment think that at the end means that there won't be cases. You know, for every infectious agent that we think of when causing a pandemic, they still come back, whether it be influenza, or potentially coronaviruses. They will, they will continue to circulate. It's a matter of how many cases occur, how many people die. And I think that's an important point. There isn't really a definition for when a pandemic ends. It's, I guess it's just when you feel like it's over. And clearly the world has come to that conclusion already. You don't need a, an epidemiologist or a politician to tell 'em that the pandemic's over that they feel that we're still seeing about 165 deaths a day in this country from Covid.(02:24):So it's hardly gone away completely. But we do have to acknowledge it. Most of those deaths are older individuals, people who have not been vaccinated recently with bivalent boosters. And in that regard, we could surely even reduce the illnesses further. I don't have any faith right now in the surveillance systems that have been set up to look at cases around the world. We've pretty much dismantled that. We are not testing people that we results in reports being made to public health agencies, whether in this country or anywhere else in the world. So I really look at two other things. One is deaths. And even they're realizing that still is a challenge in terms of how complete death reporting is due to covid. But then the other thing we're looking at, which has been really, you might say, public health revolution during the pandemic, and I say revolution cause it's really changed things.(03:19):And that is the issue of wastewater surveillance. And we've been able to ascertain in many areas of the world, in fact, with using wastewater surveillance, a much better sense of how much virus is in the community. And so, just in following with your very thoughtful comment about case numbers dropping, that's exactly what we're seeing in most locations in this country too. We, for example, here in the Minneapolis St. Paul area, have seen a dramatic decrease in wastewater activity in the last two months. So I think we're in a place right now where I can hope it'll only get better. On the other hand, you know, I have a lot of respect for this virus, and frankly, we all ought to have a lot of humility. We don't know if another variant will emerge that with, given how much immunity we have in our population will somehow break through that and cause increase in surgeon cases or whether this will become kind of the norm and we'll see less and less.On Getting Covid(04:16):Now, you asked me about my case. Yeah. I have to say that, I speak about this with, with really some trepidation in the sense that I was not gonna get this. I had and very faithful throughout the course of the pandemic, where in my N 95 respirator when I went out and about, I had been fit tested. In addition, when we finally did socialize in our home, we had a, what became affectionately known as the Osterholm Home Rule. You could not have had known contact with someone of the, with Covid in the five previous days. You could have no symptoms yourself on the day of, and you had to test negative bilateral flow test within three to four hours of coming. And we would entertain small four, the six party, parties, and it was going wonderful.(05:07):And then on March 10th, the night of March 10th, a colleague from work came over with Fern and myself. Three of us had dinner. We went down our elevator in our building here, which were 31 stories up. No one else is in the elevator. And then we proceeded to go to a very small music venue where we wore N95s. We were some distance from any other people, and we were there for an hour and 45 minutes. And, literally two days later, almost 48 hours later, all three of us developed symptoms. None of us converted for another 24 hours. And then at that point, we all three tested lateral flow positive.. We all three took Paxlovid. I took it and was starting to feel better after that fifth day.(05:59):And then I kind of crashed and at that point, I got a second, , five day course of Paxlovid and started to feel better. And, I'm you know, was very happy to have this behind me. However, over the course of the last 10 days, I have really had significant fatigue. You know I'm not one that sleeps a lot But, I can tell you there are multiple times in a day where I'm doing something like even doing what I'm doing right now where I just feel like I just need to fall asleep. It’s been really a challenge. The other thing that happened, which was in retrospect a little bit more concerning than I realized at the time, there was a period at about day 10 to 14 into my illness, I started losing my memory on many, many things of, you know, importance.(06:53):I couldn't, for example, tell you what was that drink that is: a champagne, orange juice combination. I couldn't find the word mimosa if my life depended on it. If somebody asked me who was in sleepless in Seattle, I had to think about now the movie who was in it. I couldn't remember. And I mean, in retrospect, I wasn't that concerned thinking, ah, it's not that bad. And it was actually quite remarkable. This lasted about two and a half, three weeks. And now I think, I think at least according to those around me, I have gained most of my memory back. But now I have the fatigue picture. So, as much as I don't know where I picked up the virus, all three of us picked it up. And as much asI feel like I have survivor's guilt right now in the sense that, you know, I'm not that concerned about getting infected in a public exposure given I probably have some pretty good protection, at least for a few more weeks. But nonetheless,  I think this potential fatigue issue is really a challenge.Eric (07:52):Yeah. The things that you're bringing up with this, like for example, I know you had had, the initial series and three boosters including the bivalent. Was that sometime in September last year, or,Michael (08:04):Yeah, it was seven and a half months before,Eric (08:07):Yeah. So,Michael (08:07):So, so that was, and I tried to get it at six months in the second. But in Minnesota we actually have a registry. And so it's not just your white card that, you know, you could do it. And it wasn't, I was trying to do something illegal, but you know, this vaccine's just sitting there. So I tried to get another bivalent at six months post my first one, and of course I was turned down. And then, five weeks after that I got covid.Eric (08:33):Yeah. And, and then of course, just recently the FDA and CDC finally came to the conclusion that for people of our age group and immunocompromised, they certainly have the option that you've advocated for. And unfortunately, you weren't able to get at that time. Although I suspect the protection, you might comment on that, Mike, that there is some protection infection for the first few months after a booster.Michael (09:00):Yeah. Yeah, absolutely. I mean, I think the studies that we've seen so far, at least, and particularly from those from other countries where they have remarkable follow up on databases, there is some initial evidence of protection in those first weeks against getting infected and even potential transmission. But that wanes unfortunately, quickly, and it's likely B-cell related immunity. And then I think as we all, at least believe the T-cell immunity, which we're still all trying to understand and characterize, probably kicks in and gives us protection against serious illness, hospitalizations and deaths. But as you and I have looked at even then, at six months out, you start to see some potential waning of that. And I think that's why we have a real challenge right now. I've said many, many times, we can't boost our way out of this pandemic. And I meant that not because some of us wouldn't be willing to get a vaccine every six months, but the vast majority of the population would not. And we've even seen here with the first bivalent booster dose, which we know has provided good protection against serial serious illness, hospitalizations, and deaths. Look at the very small proportion of the [age 65+] population that have taken that less than 40%. So  it's a challenge that how do we get people to keep getting vaccinated? A lot of people say, I'm done. I'm, I'm done with it.Eric (10:22):Right, right. Unfortunately, especially those who are at high risk. It's really unfortunate. Now, one of the things you've done recently among many things, you covered the status of the pandemic today and some liabilities for the future. And you've been working on the future with the blueprint that you put together from people, experts around the world to try to map out, optimally managing this pandemic’s future, preparing for the next pandemic. Could you give us the skinny on that?Michael (10:52):Well, actually this was a report that is relabeled the Covid Wars put out by the Covid Crisis Group, which was a loose affiliation of 34 individuals who had agreed to help out developing basic materials with the hope that that would lead to a post pandemic commission, much like the commission we saw after nine And then the person that headed that up actually was the person who did head up the 9/11 commission also. And there was support from several foundations for this. When it became clear, after almost a year of trying to pull together lessons learned challenges to what we know and don't know, the US government was not gonna support, another commission either at the, in the legislative side of the government or in the executive branch. Both of them basically said, well, we're not really interested.(11:48):I think that's been a major mistake. But this report, which is now out, does address a number of the shortcomings that we have experienced with this pandemic. And again, you know, in a world where it's so partisan and everyone wants to blame someone for something, this was not meant to blame. This was meant to be what we classically call a hotwash, where we go back over an experience to learn from it. What could we have done differently? How could we have done it? What did we do right? How do we have to make sure that that's in place in the future? And so this plan is, is about that very thing. Now, at the same time I'm writing another book, much like the one I did, deadliest Enemies Our War, againsts Killer Germs in 2017, when I laid out what a pandemic might look like.(12:38):And this one is really to address what do we need to learn from this pandemic for the next one? And I go into a bit more in certain topic areas, than our report did much more in depth as it relates to vaccines, public health actions, lockdowns, all of those things. And so I hope that in a, you know, a few months that'll be available so that not only does it lay out what the challenges were, but, you know, given my public health experience of 48 years and having been through these, what do I think the lessons learned should be?A Major Prediction and Being Called IrresponsibleEric (13:17):I can't wait to read it. I mean, the roadmap, though, that you've pulled together, was really extraordinary. And of course, it addressed the things like pan-coronavirus vaccine and, and so many others that we can, pursue hopefully, and be also templates for the future. Now, I want to go back now since we recovering kind of the current future status, but back in March, 2020, you wrote that there would be, this is March, 2020, there would be 800,000 deaths in the next 18 months from Covid. Talk about an oracle, I mean, obviously no one would ever wanted that to see that, be actualized, but how did you, how did you know that, Mike? How did you know we were, we were in that, in, in store for such a dreaded outcome in an imminent period of time?Michael (14:13):Well, you know, let take a step back to December of 2019. You know, our center has a very active news team that basically covers infectious disease news from around the world. Even though it's inside of CDRAP, there's a thick wall between it and me, from an editorial standpoint, so I don't have any control over it. But they notified me that they were picking up information that last week in December, out of Wuhan about this emerging outbreak of unexplained pneumonia. And,  you know, at that point we stayed on top of it. And of course, my first thought was, could this be a, a flu situation with an emerging flu pandemic, or was it just more coronavirus? You know right after 9/11, I spent three years as a special advisor to then Secretary of Health New Services, Tommy Thompson.(15:06):I split my time between the University of Minnesota and the government. And it was during that time that I actually participated actively in the first SARS outbreak that occurred, with regard to the US involvement. And then in 2012, I had been serving as an advisor to the royal family of the United Arab Emorys. And when merged first emerged on the Arabian Peninsula, I went over and worked, on that issue. And then in 2015, when MERS exploded, literally in Samsung Medical Center in South Korea, I was asked to come and I went to over to Seoul and help with that outbreak. So I had a, a pretty good feeling, I thought, for coronaviruses. And of course, influenza is something that I had been working on for 40 years. And so initially I was saying, I hold up, boy, I hope this is a coronavirus, because we know how to control that.(15:55):They're not, it's not that infectious. Even though the case fatality rates may be between 15 and 35%. Well, as you know, by the end of that first week in January, we had the data saying, yep, this was a coronavirus. But it was at that time that we had contacts in Wuhan and in Hong Kong, and we were basically getting information out. And then of course, following up with our colleagues in Singapore, the old flu network that was suggesting that this was a very different kind of coronavirus. This, there appeared to be substantial transmission among those who were asymptomatic as well as those who were symptomatic. And as we saw more and more transmission, outside of, of Wuhan, it reminded me of great deal of what we saw in 2009 with H1N1, where there in the month after it was first discovered in Mexico, it was subsequently found in 128 different countries in just one month.(16:52):And, and it looked like this is what this coronavirus was doing. And so on January 20th, actually our center put out a statement saying, get with it world. This is the next pandemic. It is a coronavirus acting differently than MERS and SARS, my worst fear was that the case totality rate may be as high as that. Well, over the course of the next few weeks, we got more in better information about what was going on. And there was just such a denial at the time. In fact, I went to JAMA, and to the editor's and said, can I do a perspectives piece on why the world has to wake up quickly? This is going to cause a pandemic. They not only turned me down, but the following week they ran a cartoon in JAMA, one pager on one column looking at Covid, and Coronavirus is on the right kind column looking at influenza.(17:43):And they came to the conclusion, don't get distracted by this coronavirus thing, it's about flu. Wow. And so I think at that time, there was such denial that was going on. So when I first made this statement, I actually did it by the kind of the back of the seat estimate. You know, I'm not a black box guy. I, in fact, I find black boxes often, they sort of press the hell outta you with their sophistication. And what they don't tell you is they have no clue what they're talking about. So I just basically did a back to the envelope calculation and not even realizing vaccine might or might not come into place. So, you know, I have to be honest and say it was in some ways luck.Eric (18:29):Yeah, I don’t know, I think it's a lot of wisdom and mixed with that.Michael (18:33):You know, I want to add one, I want to add one of the thing though, Eric, because the thing that I will most remember probably in this pandemic is not all the hate mail that I received from so many as the days went on and even death threats. It was the feedback I got in that month of March from colleagues who thought that I was over the top that I had finally, you know, scared the hell out of people one too many times kind of thing. And it was amazing to me, as much as we're critical of the politicians and what happened, and we surely should be, there were many of our colleagues who were equally in a state of denial not wanting to believe that this was really happening.Eric (19:15):Oh, absolutely.Michael (19:16):Yes. So I think that's what I'll remember is, it's one thing to have some anonymous person tell you, you know, that you should be dead. It's another thing to have one of your colleagues say you're irresponsible.Organizing the “Party Planning Group”Eric (19:29):Yeah. You're not kidding there. And you know, especially with you because you know, everybody who's listening has seen you innumerable times on, you know, CNN, MSNBC, Meet the Press, and, various news networks, and they know you come across with humility, unlike many other experts where, you know, you say we just don't know. And also the master of metaphors, as far as I can tell, like the eye of the hurricane and so many things like that. But the other thing I wanted to get into historically is something that brought us together that a lot of people still, it's been written about, but a lot of people still don't know. So back in the summer of 2020, you said, I'm gonna organize a group, a group that eventually became known as the party planning group that we meet every Friday morning for an hour or so. And we talk about, well, there's a pandemic and related matters. So you again, had this idea to bring this group together. And could you talk about that, because it's amazing here it is. You know, two and a half years later we met today, we are, we're continuing to meet, tell, tell everybody about whether that group, how, how you first saw the need for it. And perhaps, you know, what do you think it's accomplished?Michael (20:43):Well, first of all, let me start out with two caveats, number one, and thank you for your comments. But I realize the older I get, the more vulnerable I am to learn . And so I want to surround myself with people that can teach me. Okay. The second thing is, is that humility should be considered a requirement today of trying to deal with pandemic viruses because we have to acknowledge, we don't know what the next major curve ball is going to be. You know, I can remember a, a a light bulb moment for me early in January of 2021 when vaccines were now flowing. But recall you and I together, we wrote a piece on this. So Alpha variant emerging out of Europe. And remember up until then, that time, we kept being told that, well, these variants, the sub variants are really just nothing more than rings on a tree.(21:36):They're just telling you how old the virus is. And with Alpha, we had clear and compelling evidence, oh no, it had a lot to do with functionality, how infectious it was, et cetera, and that that could very well change the complexion. And I remember very well, being on Meet the Press in January of 2021 and saying, I thought the darkest days of the pandemic were still ahead of us because of the number of people who were not vaccinated. The fact that this viruses was going to continue to change. And of course, again, I caught a lot of heat for that Nate Silver, who gutted me in public media for irresponsible. And of course, as you know, the vast majority of deaths occurred after that time. Right, right. But now to back up to your point and why I think some of the things that I was able to learn occurred was in the summer of 2020, a colleague of mine who, very near and dear came to me,  said that there is someone in the senior level of government that right now is making some major decisions, but really has no one around him he knows he can trust.(22:42):Would you ever talk to him and, and provide what information you can to kind of give him a sense off the record? Well, I thought, you know, actually it would be better cuz there's a team of people I think that could be more helpful. I'm one, I'm one voice and I surely don't proclaim to have the only voice. So I actually literally went to my might say, magical list, who are the people that I most respected and admired, and who did I trust? And trust was huge. Trust was huge. And as you know, you're on that list. it's now been publicly stated. Peggy Hamburg. Peter Hotez, Bruce Gellen, Pennny Heaton and, Ruth Berkelman. And you know, we, we meet on every Friday and our discussions are incredibly, incredibly thoughtful.(23:39):They are honest and there's a trust in that group. You know, what we share stays there. And I, I so appreciate that. And so from that perspective, that will continue and I will continue to learn from all of you. And I think if it was any one lesson that came out of from this pandemic is just the value of having that kind of collective brain trust that can come in, ask questions. Many times we didn't have the answers, but we surely got the questions out, which then gave us opportunities to learn the answers. And the fact that we could do it. And you and I both knew that our comments were gonna stay within the context of that group.Eric (24:20):Yeah. And we had to keep it anonymous with this name of party planning group just because we didn't want people to know what this was.Michael (24:29):Yeah. At that time, it was interesting. I have to tell my administrative assistant was out one day during that time, early time period. And someone else, was sitting in and they saw in my schedule an hour blocked off for party planning. And it was right at the holiday season. So there was an assumption made in my, in our center that I was just planning this big holiday party and that nobody knew about it yet and said party planning. And that rumor got spread got, was spread throughout the entire center. And I had to self-correct, you might say and explain we can still have a party, but that wasn't what this was about.Eric (25:07):Yeah. Well, it, it's been an amazing ride and it continues, but, you know, we were there from well before, there were vaccines all the way through to the current time. And you can imagine all the different things that have been happening in the background and that we were discussing, exchanging ideas, communicating with the public health agencies, the White House and all sorts of other issues along the way. So it's been a privilege for me not just to have this conversation, but over these last two and a half years to work with you on that. It’s been extraordinary and to learn from you and our colleagues. Well, this has been so much fun for me, Mike, I I just am struck by your ability to weave together, you know, the, the wisdom you've drawn from all these experiences over four decades of working in this space with the ability to be humble and know that, you know, you're not the smartest guy in the room.(26:07):No one's the smartest guy in the room that you want to have other people, you know, whether, wherever they come from, like for example, when you put together the Roadmap and you brought together, you know, people from all over the world, to think, to exchange ideas about how we can do better for this and future pandemics, because undoubtedly we're gonna be facing those. So maybe, as we wrap up, could you just give us your sense, there's obviously climate change, there's all the things that have been done to the environment and this pandemic, which we all want, wish to be, you know, put aside, the virus will be here for many years to come. But what are your expectations since unfortunately your predictions have come too close to real, about the next pandemic. Will it be influenza? Will it be in the next few years? What are your thoughts about where we're headed?Michael (27:05):Well, you know, Eric, let me just start out and say thank you for your very kind comments. I think one of the things I learned at CIDRAP a long time ago is the very name, the Center for Infectious Research and Policy. And I knew very early in my career that well designed, well conducted even very important research means nothing if you can't translate that into active policy that makes a difference. At the same time, policy, if it's not informed by good research can be dangerous. And so I think what you're highlighting here is how we try to bring groups of individuals together to merge research and policy together. And you just talked about the Coronavirus vaccine roadmap, where 54 of the world's leading experts, including you, participated in that. And we developed a very, very specific, outline for a roadmap of what needs to be done to get us to new and better coronavirus vaccines, and ones that basically, will be hopefully broadly protective for any future coronavirus activity that occurs.(28:12):So I can never say enough about the ability to bring shareholders together. Collective wisdom will win every time against a wisdom. And I think that that's one thing I learned in terms of where we're going. You know, I, I have to just think back into human history. And when you think about the fact that in 1900 average life expectancy in this country is about 43 years, and today, even with the pandemic, it's about 76, 77 years. For every three days we've lived in the last century and 20 plus years, we've gained one day of life expectancies that takes us all the way back to the 80,000 generations to the caves. And I think what we haven't fully understood is, is that we lived in a world where infectious diseases had major impact on why we didn't live to be as a median age, life expectancy, up into the seventies, but rather, into the forties.(29:13):And I think what we're facing today is a world that's moving us back to those, numbers not forward. For example, if you look at just the situation right now of world population, 8 billion people on the face of the earth you look at, you know, what's happening with mega cities around the world. You know, I, remember early in the days of HIV aids making a trip to Kinshasa, which no longer is of course, where it was a large rural city. Today it's 18 million people. When you look at the median age of Africa, it's 19 years. When you look at what we've done with human population and how we have reached out to every corner of the world seeking food, bush meat, et cetera, I, you know, Ebola has been a problem likely for many, many, many decades. But when it was in very rural, isolated villages of Africa, you know, if 25 or 30 people got infected and died, no one even knew about it.(30:19):Now, today with the organization of Africa, you can see widespread  transmission quickly in these areas. And this is true for all parts of the world. Think about avian influenza and the need today to feed 8 billion people. We have relied on  birds on, on the fastest that as an animal species is the fastest conversion of energy to protein on earth. And so look at the billions of birds we're raising, which now provide for a new reservoir for flu viruses. I can go down the list, look at how climate change is moving, in terms of precipitation levels and temperatures that now move mosquito populations to places of existence we didn't see before. And then added transportation in. Think about all of history to World War II, the four serotypes of Dengue virus existed in four different regions of the world. It wasn't until First World War II that now they all exist virtually where each one exists.(31:19):Why do we have Dengue hemorrhagic fever? It's because of that. And so I think that the final piece I would say is yes, pandemic's gonna happen again. We are going to see more of what we've just experienced. And frankly, it could be a lot worse. We didn't see 15 to 35% mortality rates like you might with SARS or mers, but instead we saw just high transmission levels. There is nothing to stop the next coronavirus from being transmitted like SARS -CoV-2in killing like MERS or SARS. Mm. And so I think we have to be mindful of that. And the final last thing, I would just paint this is our climate change issue in infectious diseases. It's antimicrobial resistance, it's amorphic, people all know it's there, what to do about it. And we are watching ourselves literally devolve back into a pre pandemic era of antibiotic resistance.(32:14):Meaning that, you know, before our grandparents were around, people died often from common in, you know, cuts, bruises, et cetera, because they didn't have antibiotics. Look what's happened since that time, they've played a huge role. Sure. And now we're gonna watch that. You know, we're wildly that. And then last one, at least, I just have to say misinformation, disinformation on vaccines is huge. I think that we're gonna continue to see increasing challenges with populations around the world, no longer willing to take childhood immunizations or even other adult immunizations just because of the disinformation. So when you add that all up, it's job security, unfortunately, for a lot of us. And that's a sad commentary. It's real, yeah.Eric (32:58):Well, and as you pointed out so well, just before we got started with AI, it has a potential to amplify the myth and disinformation to unprecedented levels. And it's already so, you know, horrific as it is.Michael (33:13):You know, it's bad enough that I can just say that there are times I read articles in newspapers, and I'll get halfway through a quote and I'll say, who the hell said that? According to Osterholm? And of course, what? ,Eric (33:28):Right. There you go.Michael (33:30):What are we gonna do when you and I end up on these bots? You know, we're there Is Eric Topol saying, saying to the world, you know,  I was wrong. Vaccines aren't any good. Yeah. And people are gonna see that, and it's not you. Right?Eric (33:44):Right.Michael (33:44):A lot that concerns me a lot.Eric (33:46):No, it, it was deep fakes and now it's going to another ultra level of that. It's pretty scary actually. So with all the things that we've been talking about, whether it's a potent virus or a tech like AI is becoming with generative AI we've always gotta look at both sides of this and, and be prudent, to put it mildly. Well, this has been fun. And I can't thank you enough. I I, I would like to talk to you all day, but we've got a got a lot in there in a half hour, and I know we'll get a lot of interactions from the folks that are listening. Mike, thanks.Michael (34:25):Gift to all of us. You're a gift to all of us. Thank you.Eric (34:28):Oh, thank you. That's much too kind. Get full access to Ground Truths at erictopol.substack.com/subscribe