In this episode we discuss a novel idea in the healthcare and AI space: using Swarm Learning and blockchain technology for decentralized and confidential machine learning on clinical data. This promising new framework for collaborative research improves both algorithm performance and preserves patient privacy.  This idea has been pioneered by Dr. Joachim Schultze, who recently published an exciting new paper on the subject in Nature. Dr. Joachim Schultze is a professor of Genomics and Immunoregulation at the DZNE in Germany and the University of Bonn. 2:30 Introduction and Background 6:00 Studying Broadly as an Academic 9:10 Joachim Schultze's introduction to A.I. through work on Leukemia 14:45 Recent Nature Paper - Swarm Learning & The Blockchain 21:10 Federated Learning vs. Swarm Learning 23:00 Using the Blockchain and Smart Contracts to Secure Data Sets 25:00 External Threats to the Swarm 29:40 Reaching Agreement Before Inter-Institutional Swarm Learning 35:50 Utilizing Multiple Nodes to Answer a Clinical Question 39:18 Reducing Technology-Driven Noise and Decreasing Bias With the Swarm 44:50 Open Science, Open Insights, But is Open Data Absolutely Necessary? 46:46 The Necessity of an Interprofessional Team to Complete This Project 48:30 Next Steps For This Project 54:10 Central Maintenance For The Swarm 55:10 Future of A.I. in Medicine 59:40 What Advice Would You Give To Your 20-Year Old Self Interviewer: David Wu Producer: Aaron Schumacher & Alexander Jacobs Art: Saurin Kantesaria @saorange314 - Instagram

Professor Glenn Cohen is a James A. Attwood and Leslie Williams Professor of Law at Harvard University. Professor Cohen is one of the world’s leading experts on the intersection of bioethics and the law and is the author of more than 150 articles appearing in such places as New England Journal of Medicine, JAMA, The American Journal of Bioethics, The New York Times, and The Washington Post. He also leads the Project on Precision Medicine, Artificial Intelligence, and the Law, which is part of the larger Centre for Advanced Studies in Biomedical Innovation Law. In this interview, we discuss a variety of legal and ethical topics like data privacy, liability and medical errors, and AI use disclosure in patient settings. Professor Cohen provides many examples of how AI is changing the face of our society from driverless cars to Target knowing us better than our own family members! He also makes a few great literature and media recommendations: "Exhalation" by Ted Chiang, "The Paper Menagerie" by Ken Liu, "The Three-Body Problem" by Liu Cixin, and of course, the Netflix original, "Black Mirror." P.S. Follow professor Cohen on Twitter (@CohenProf) for more nuggets of wisdom on legal and ethical issues in artificial intelligence (and in many other healthcare sectors)! 1:30 Professor Cohen's Journey 3:17 Project on Precision Medicine (PMAIL) 5:46 "Case-based" approach 8:57 Who takes the blame? 11:20 Driverless cars and healthcare 12:33 Medical errors 13:08 Big data, HIPPA 16:30 Where are we going? 18:40 Bias in AI + Healthcare 20:00 Advice to your past self! 22:30 Vital interprofessional collaboration Interviewer: Madeline Ahern Producer: Melanie Bussan Art: Saurin Kantesaria @saorange314 - Instagram

Dr. Vivian Lee, MD, Ph.D., MBA is currently President of Health Platforms at Verily, an Alphabet Company. Dr. Vivian Lee is also the author of the latest book “The Long Fix,” a book about solving America’s healthcare crisis. Dr. Lee has accomplished much in her diverse career. She received a doctorate in medical engineering from Oxford as a Rhodes scholar, her MD from Harvard Medical School, was valedictorian at NYU Stern School of Business, authored over 200 peer-reviewed research publications, as well as a cardiovascular MRI textbook, former CEO of the University of Utah Health and dean of their medical school and, is now the President of health platforms at Verily Life sciences, an Alphabet company. In this interview, we talk about her journey to Verily today and her thoughts on how healthcare has been changed for the better by new technologies like Digital Health Platforms, an example being Onduo for blood glucose management in diabetics. We also talk about how medicine has changed from the days she started medical school to the future landscape that current medical students face today, one that is much more integrated with payers, tech, politics, and employers. We hope that this interview inspires you as it did to us to try and tackle all of healthcare’s problems with renewed vigor. Thank you and enjoy! P.S. Please check out Dr. Vivian Lee’s latest book “The Long Fix” and review it on Amazon/GoodReads!! 2:50 Dr. Vivian Lee’s journey 8:20 Transition to Radiology 12:40 Transition to Univ. of Utah 15:50 “What does your job at Verily entail?” 17:10 Onduo - an example of Health Platforms in action 23:50 Verily and COVID testing 27:40 “The Long Fix” and the Co-Production of Health 37:00 MedSchool now vs. MedSchool then 44:00 Verily and how it affects the future of medicine 46:00 David’s misattributed Luddite fears 50:00 What advice would you give your younger self? Interviewer: David Wu @davidjhwu - Twitter Producer: Aaron Schumacher @a_schu95 - Twitter Art: Saurin Kantesaria @@saorange314 - Instagram

Dr. Faisal Mahmood is an Assistant Professor of Pathology at Harvard Medical School and Computational Pathology at Brigham and Women’s Hospital. Dr. Mahmood recently published an exciting new paper this year where he and his team built a deep learning model to accurately identify tumors of unknown origin on pathological slides (Lu et al., Nature 2021) Pathology is one of the central pillars of medicine and here we really dive deep into how machine learning is pushing the boundaries of the field and our abilities to diagnose and recognize tumors. Enjoy! Twitter: @TheMaMLPodcast Interviewer: David JH Wu (@davidjhwu) Producer: Aaron Schumacher (@a_schu95) Cover Art: Saurin Kantesaria 1:20 Background in computational pathology 5:30 Interest in Pathology 10:20 Modern algorithms detecting biomarkers to better educate physicians 12:05 Using AI to identify tumors of unknown origin 17:10 Building the TOAD AI Model 21:50 Assessing the validity of the Toad Model 23:30 Determining inputs for the TOAD Model 26:30 Diversity with the TOAD Algorithm 27:40 Next steps for the project 33:15 Using AI to augment physicians' abilities 35:06 Advice for physicians interest in AI 37:00 Dream Research Project 38:40 Will AI make medical discoveries in the future? 40:38 Advice you would give to yourself in your 20's 41:55 Obtaining a Ph.D. in Japan 44:00 Closing thoughts Paper: Lu et. al, “AI-based pathology predicts origins for cancers of unknown primary.” Nature, 2021

Dr. Nneka Comfere is a Dermatologist and Dermopathologist at the Mayo Clinic in Rochester, MN. We discuss Dr. Comfere's discovery of visual beauty within dermatology and how this can be applicable in a machine learning setting. We also talk about the possible uses for dermatoscopes and artificial intelligence to fill gaps in care based on location. Dr. Comfere's take on AI from a clinician's perspective is accessible to not only medical professionals, but also those seeking to learn more about how machines are becoming part of the healthcare system. Enjoy! Interviewer: Maddie Ahern 0:25 - Journey to Dermatology and Dermopathology, Integration of AI 9:35 - Articles in Journal the American Academy of Dermatology 19:25 - Initial Venture into AI, Building a Dermatological Database 28:32 - Future of AI in Medicine 32:15 - What is Next for Dr. Comfere? 36:51 - Advice for Students/Learners

Ian Pan, MD, is a Kaggle Grandmaster, radiologist resident at the Brigham and Women’s Hospital, and a rising star in the medicine and AI field. Kaggle competitions are international data science competitions that are both very competitive and prestigious. We talk about Ian's path to medicine and AI as well as the various strategies he’s used to become one of the top coders globally in this burgeoning new field. Ian also gives some great advice on how to get started and we close with some of his exhortations against poor practices in ML today. This interview was a lot of fun and if you are curious about Kaggle competitions or how to be the best at them, this interview is for you. Time-Stamps 6:20 Initial interests in Radiology 12:50 2018 Pneumonia detection Kaggle challenge 17:55 Domain expertise not necessary for AI learning 20:18 How to approach an AI challenge 25:23 The structure of Ian's Kaggle-winning models  28:58 What sets Ian's models apart 32:30 Non-medicine endeavors  37:40 Coding Background 39:00 Should medical students learn to code 42:00 The future of AI in medicine 49:10 What’s next for Ian 54:07 Necessary changes to AI in medicine 57:15 Advice for medical students

Anouk Stein, MD, is a radiologist and AI Data Specialist at MD.ai, a healthcare start-up based in NY. We discuss Dr. Stein's journey to MD.ai as well as her current work in the medical AI space. Dr. Stein provides some great advice for anyone looking to get started in practical machine learning. We also talk about some of the exciting kaggle competitions held by MD.ai as well as the importance of external data validation. We close with some great advice for our listeners from Dr. Stein on how to embrace the exciting new changes taking place in medicine today. Dr. Stein is a terrific teacher and I learned a lot from her in this interview. I hope you all enjoy! Timestamps  1:10 Individual path to healthcare and AI  3:58 What does MD.ai do?  5:00 Stanford Design-Your-Life course  7:40 AI and Radiologists  12:00 Combining algorithms and the necessity of a meta-algorithm  14:48 External Validation of Data   16:50 Practical Machine Learning  22:10 What is external validation   25:10 Controversy over generalization   29.10 Accomplishments of MD.ai  32:30 What is it like to work in industry after medical education   37:45 How MD.AI got started   40:25 Fields of Medicine looking towards AI  43:40 The future of AI in medicine over the next 10 - 20 years   46:25 What advice would you give to yourself in your twenties   47:00 Any advice for young physicians Resources Mentioned: Kaggle - Python tutorial Fast.ai Facebook Detectron 2 Pandas

Judy Gichoya, MD, MS, is an interventional radiologist at Emory University in Atlanta. We begin by talking about Dr Gichoya’s early days in Kenya where she participated in building OPEN MRS, the world’s leading open-source EMR platform. We then talk about her work in using AI to combat bias and social injustices in medicine and the importance of diversifying the datasets we use in AI work today. 03:00 Origins in Kenya, building OpenMRS, path to AI 14:00 Research topics of interest in the Gichoya Lab (Emory) such as bias in AI 21:00 steps we can take to combat bias in datasets 27:00 work on federated learning 36:00 advice to medical students / early-career med students interested in the field 43:00 balancing clinical work and informatics research 50:00 favorite food from hometown!

Daniel Tse, MD, is a product manager at Google Health. In this episode, we talk about his unconventional path to healthcare and AI, and discuss the career angst that many may feel when they wonder that if the path they are on is the right path. I can’t say that we’ve discovered the perfect answer in this interview, but I will say that Dr. Tse is someone who has thought deeply about this question as you can see from his unique career and will probably provide some good words of advice. Had a real pleasure recording this interview. Hope you all enjoy! Interviewer: David Wu Producer: Aaron Schumacher 1:30 Childhood + Budding Interest in Computers 8:00 Path to medicine 12:00 Working at a healthcare startup during medical school 20:00 Working at Google after graduating medical school 30:00 Comparing and contrasting Google with an academic research lab 35:00 MD training + how it relates to working at Google 38:00 What does a product manager do? 45:00 What do you expect is the future of AI + medicine? 49:00 What advice would you give to your 25 yr old self? 52:00 Advice to people who are questioning whether medicine is the right path for them

Jack Moore is a product manager at Qventus, a healthcare start-up based out of Mountain View, California. In this episode, we go into depth about how he and his team are using machine learning to improve the efficiency of hospitals such as the University of Minnesota’s MHealth system. One example we discuss is how Qventus builds and deploys models to improve patient care and reduce ICU bed wait times. At the end, we spend time talking about bias and race in medicine and how we can use AI to combat the propagation of such biases. Interviewer: David Wu Producer: Aaron Schumacher Time Stamps: Path to healthcare AI and Qventus: 01:00 What does Qventus do 04:15 Story behind Qventus 09:00 What if the AI model and physicians disagree? 15:00 Structure of the Qventus AI models + explainability 21:00 Integration with Epic 30:00 What Qventus use looks like as a provider 36:00 Cool definition of data science 42:00 AI to reduce health inequalities and racial bias in medicine 58:00