Impact AI

The climate crisis is one of the most important and complex challenges of our age, and solving it will require collaboration, innovation, and commitment. According to Project Drawdown (a non-profit organization that functions as a top resource for climate solutions), one of the key drivers of climate change that we can meaningfully address as a society, is food waste.In today’s episode, we learn about Afresh, a company that is leading the way in providing food waste solutions to grocers across America by creating optimized food orders through pioneering AI and machine learning solutions. You’ll hear from Afresh Co-Founder, Nathan Fenner, as we discuss the founding mission behind the company and how they are leveraging AI in a way that is fundamentally different from other established legacy companies in their field. We discuss the challenges of working with perishable products, how it results in noisy data, and why it’s so important for Afresh technology to not only provide predictions but also make decisions in the face of uncertainty.Today’s conversation unpacks a particularly exciting area of AI and demonstrates how advancements in the field are paving the way for impactful climate solutions. Be sure to tune in to learn about the real-world impact of AI innovation in an area where we need it most urgently!Key Points:Get to know today’s guest, Nathan Fenner, and how he co-founded Afresh.Why reducing food waste is a key part of mitigating climate change.How Afresh is helping the grocery industry optimize supply chains for perishable products.The role that machine learning plays in Afresh’s technology.An overview of the three main sources of data that they feed into their system.The biggest challenges they experience with their data sources.Understanding how past retail system solutions were built with non-perishable items in mind.Why perishable items result in extremely noisy data.The challenges that noisy data poses to machine learning models.How Afresh is addressing the challenges inherent to noisy data.What differentiates Afresh from other established legacy companies in their field.How Afresh is leveraging AI to make decisions, rather than simply providing a forecast.How Afresh measures the impact of their technology on profits, food waste, and the planet.Unpacking the difficulty in finding, hiring, and attracting machine learning specialists.The confluence of factors that are helping Afresh attract top talent.What Nathan is most excited about for the future of Afresh.Quotes:“We're hyper-focused on building supply chain software to optimize all the perishable supply chains in retail. The big outcome of optimizing that supply chain is that we dramatically reduce food waste. Food waste is one of the biggest macroscopic contributors to climate change.” — Nathan Fenner“Good machine learning is key to writing an optimal order that maximizes profit, but also minimizes waste.” — Nathan Fenner“All the technology that had been built for the grocery industry, and that was being used in supply chain and inventory management, had all been built for the non-fresh side of the business. It had all been built for things that come in boxes that have barcodes.” — Nathan Fenner“We leverage AI in a fundamentally different way. We definitely do forecasting, but the critical thing we're doing is really decision-making under uncertainty. The output from our models is actually a decision as opposed to simply a forecast.” — Nathan Fenner“Leveraging this more frontier area of machine learning has allowed us to make really good decisions in a really uncertain environment.” — Nathan Fenner“If we can build a technology that reduces food waste by 50%, it will become uneconomic for grocers to not use our technology (or a similar technology) that produces that much in cost savings.” — Nathan FennerLinks:Nathan Fenner on LinkedInAfreshResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today, I am joined by Ersin Bayram, the director of AI and data science at Perimeter Medical Imaging AI, to talk about tissue imaging during cancer surgery. This technology provides real-time margin visualization to surgeons intraoperatively vs. waiting days later for the pathology report, which remains the gold standard for confirming margin status. The surgical oncologists’ goal is to achieve clean margins on excised tissue during the initial surgery and reduce the chance of the patient requiring a second surgery or leaving some cancerous tissue behind. The next generation of this device uses AI and big data to speed image interpretation.Tuning in, you’ll hear about the role of machine learning in this technology, how they gather and annotate data in order to train the system, and the types of challenges encountered when working with OCT imagery. We discuss the role of model explainability, whether or not model accuracy is more critical, and how classic activation maps are used for improving the model. We also talk about regulatory processes as well as Ersin's approach to recruiting and onboarding before he gives his advice to other leaders of AI-powered startups. For all this and more, tune in today!Key Points:An introduction to Ersin Bayram and his role at Perimeter Medical Imaging AI. What Perimeter does and why this is important for cancer outcomes. The role of machine learning in this technology. How this technology segments out potential areas of concern on the OCT scans and displays this to the surgeon.  How Perimeter Medical Imaging AI gathers and annotates data in order to train the system.The types of challenges they encounter when working with OCT imagery. The role of model explainability and whether or not model accuracy is more critical. How classic activation maps are used for improving the model and not shown to the clinician.  How the regulatory process affects the way Ersin and his team develop machine learning models. Approaches to recruiting and onboarding that have been most successful. Ersin’s advice to other leaders of AI-powered startups. How he foresees the impact of Perimeter three to five years from now.Quotes:“I can still work on oncology, making an impact on a really deadly disease, and also start focusing entirely on the AI side and the data science aspect. That was an easy decision.” — Ersin Bayram“The surgeon might be able to look into the images and then they might be able to go back and take extra shaves or there might be also benefits not to carve out healthy tissue more than needed.” — Ersin Bayram“If you find the talent that has the medical imaging background and they have the foundational skills, technical thinking, and they have basic Python skills, we can train them and we can ramp them up to become good AI scientists.” — Ersin BayramLinks:Ersin BayramPerimeter Medical Imaging AIDisclaimer:Perimeter B-Series OCT is not available for sale in the United States. CAUTION – Investigational device. Limited by U.S. law to investigational use.Resources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Matt Gray is the co-founder and CEO of TransitionZero, a not-for-profit analyzing financial data to manage the decline of fossil fuels and support the shift to zero carbon growth opportunities. During today’s conversation, we talk about how TransitionZero leverages the power of machine learning to create a positive impact on the world. We touch on the mechanics before acknowledging the indispensable role of domain expertise in creating success. We talk about measuring impact in a not-for-profit context, and zoom in on TransitionZero’s mission and projections for the future. You’ll hear some examples of the incredible change that has taken place as a result of the TransitionZero's work, what some of their challenges have looked like, and the exciting inner workings taking place at TransitionZero today. Thanks for tuning in! Key Points:An introduction to Matt Gray, co-founder and CEO of TransitionZero. What TransitionZero does and why it is important for reducing emissions.The role of machine learning in data analysis.TransitionZero and the Climate Trace organization. How TransitionZero validates models.Why domain expertise is indispensable.How the approach differs between different facilities.Measuring impact in accordance with TransitionZero’s mission.Examples of the impact the organization has had in China and Japan.The challenge of finding the right people to join the team. Policies that enhance non-salary benefits to the team.His advice not to lead with AI.How TransitionZero is approaching the Future Energy Outlook Project. His hope for the future of TransitionZero’s impact.Quotes:“TransitionZero is a climate data analytics not-for-profit, co-founded in 2020.” — Matt Gray“Another application we are just embarking on is using data science to estimate the productivity of wind and solar assets.” — Matt Gray“One thing we have learned over the last two to three years is that domain expertise is indispensable when you’re building models.” — Matt Gray“Our mission is for affordable and dependable energy for everyone.” — Matt Gray“Don’t lead with AI. Lead with the use case and the problem that you are solving.” — Matt GrayLinks:Matt Gray on TwitterMatt Gray on LinkedInTransitionZeroTransitionZero on TwitterTransitionZero on LinkedInTransitionZero on GithubClimate TraceResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today, I am joined by Kilian Koepsell, co-founder and Chief Innovation Officer of Caption Health. We’re taking on the multifaceted topic of ultrasound for early disease detection. Join us as Kilian talks about the problem Caption Health identified in the world of ultrasound use, and how he is working to solve it. Hear how he is using machine learning to help practitioners to guide and interpret ultrasound imaging, why his first point of entry was cardiac health, and where the role of the machine ends and the medical expert begins. Kilian shares some challenges he has faced along the way, and encourages anyone with a similar idea to approach the FDA sooner, rather than later. Tune in today to hear how his concept aims to support healthcare in a changing world, and how he sees the future of Caption Health unfolding. Thanks for listening!Key Points:An introduction to Kilian Koepsell, co-founder and Chief Innovation Officer of Caption Health.What Caption Health does and why it is important for imaging. Why there was a hurdle to get ultrasound technology used by more people.The two kinds of feedback Caption Health provides: guidance and interpretation.How machine learning is used to perform these two functions.Why their first focus is on the heart and why it is one of the most difficult organs to image. Measurements taken by the machine for a practitioner to interpret.How the quality meter works to guide the probe and gives practitioners the feedback and confidence they need.Challenges in training machine learning on ultrasound imagery. Validating models across many variations.Why it is so important to take FDA considerations into account from the beginning.How Kilian ensures that he is developing technology that will be of use to practitioners.How the Caption Health vision has changed since its inception.Having a high level thesis to survive a changing world.Where Kilian sees the impact of Caption Health in five years.Quotes:“We realized that even though the hardware was available at a much lower cost to many more people, there was a big hurdle to get the ultrasound used by more people because it is actually very difficult to acquire good ultrasound images.” — Kilian Koepsell“We use machine learning to understand the relationship between the imagery and the position of the probe in 3D space, and then guide the user to the right spot without the user having to even understand what they are looking at.” — Kilian Koepsell“By just looking at the imagery you can see if the heart is not pumping well or if it’s enlarged or if the valves are not closing properly - all different kinds of structural heart diseases.” — Kilian Koepsell“Normally you would require an expert to look over their shoulder and give them the feedback, but with this device, they can train themselves, and they get better over time by using it on patients.” — Kilian Koepsell“Anyone who is trying something similar, I would encourage to get in contact with the FDA as early as possible.” — Kilian KoepsellLinks:Caption HealthKilian Koepsell on LinkedInKilian Koepsell on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Climate change is impacting many industries across the globe, and the farming and agriculture industry is no exception. Changes in rainfall patterns, temperature, and increasingly extreme weather place farmers under immense strain and threaten food security. However, there is a solution, and Manal Elarab, the COO at Regrow Ag, is here to explain how Regrow is changing the farming industry.Regrow Ag aims to empower food and agriculture industries to adopt, scale, and monetize resilient and regenerative agricultural practices. We start by learning about Manal, her professional career journey, the how and why behind Regrow Ag, and the company’s overall mission. We then discuss why agricultural practices need to change, and unpack the complex relationship climate change has with agriculture. Hear about how Regrow Ag is leveraging machine learning to enhance regenerative farming, what makes regenerative farming practices different, the different technological toolkits Regrow Ag has developed, and more. Tune in to discover how technology is being used to revolutionize the farming industry with Manal Elarab from Regrow Ag!Key Points:Hear about Manal’s professional career journey and what led her to Regrow Ag.What Regrow Ag does and why it is important for agriculture and climate change.Learn about regenerative agriculture and how it differs from other forms of agriculture.How Regrow Ag leverages machine learning to help achieve its mission.Manal explains what data is collected, how it is collected, and how it is used.Overview of the challenges encountered when working with remote sensing data.Learn how the models used can account for different types of variations in data.How Regrow Ag engineers collaborate with other experts in order to get the required data.Their approach to measuring the impact of the technology and solutions implemented.Manal shares advice and insights for leaders of AI-powered startups.What to expect in the near future from Regrow Ag.Quotes:“Compared to other industries, agriculture almost finds itself on both sides of the climate change equation. It is what agriculture is doing to the climate and what climate change is doing to agriculture.” — Manal Elarab“Machine learning plays a big role at Regrow. Our core offering is built on two key elements. A process-driven carbon model and the machine learning-based toolkit model.” — Manal Elarab“I would say machine learning is a tool that powers a product.” — Manal Elarab“I imagine myself and my kids walking down the grocery aisle, picking up snacks and cereal boxes, and pasta, and selecting products that the growers have used regenerative practices in producing those crops.” — Manal ElarabLinks:Manal Elarab on LinkedInRegrow AgRegrow Ag BlogResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Over the past few years, there has been a concerning rise in rates of depression, anxiety, and other mental health disorders, especially among adolescents and young adults. In addition, the current state of our national healthcare system is not set up to offer equitable access to all, depriving a huge portion of the population of the help they need. The new platform, Kintsugi, has been taking important steps to address these shortcomings by developing AI learning models that detect signs of depression and anxiety from speech samples.Today on the show I welcome Rima Seiilova-Olson, Co-Founder and Chief Scientist at Kintsugi, to talk about the current state of mental health care and what Kintsugi is doing to offer support to the many individuals who need it. I talk with Rima about her difficult experience with postpartum depression, her subsequent struggle to access mental health care, and how these events (combined with her expertise as a software engineer) led her to co-found Kintsugi. Rima goes on to explain how Kintsugi can be used as a tool by mental health professionals, and the benefits of incorporating it into clinical workflows. We also discuss some of the biggest challenges of working with speech data, the systems they are putting in place to combat bias, and how psychiatrists are helping them validate their speech data. To learn more about this incredible technology, and the life-altering impact it could have, be sure to tune in today!Key Points:Get to know today’s guest, Rima Seiilova-Olson, Co-Founder and Chief Scientist at Kintsugi.The set of circumstances that inspired Rima to create Kintsugi.What it means to bring quantifiable and scientific measures into the field of mental health.How Kintsugi detects signs of depression and anxiety from speech samples.Understanding the benefits of incorporating this technology into clinical workflows.The integral role of machine learning in this technology.The limitations of traditional healthcare systems.How Kintsugi is helping more people gain access to mental health care.Why Kintsugi uses psychiatrists to collect and validate data.The biases that can occur in models trained on speech data.The measures Kintsugi has put in place to mitigate these biases.Some of the biggest challenges of working with speech data.Rima’s insights on the potential financial, clinical, and emotional impacts of this new technology.Rima’s advice to other founders of AI-powered startups.Why it’s so important to combat the trend of digital health companies prioritizing financial gain over clinical impact.The expected impact of Kintsugi over the next five years.Quotes:“My co-founder and CEO, Grace Chang, and I put our heads together and decided to start by bringing quantifiable and scientific measures into the field of mental health. Which has mainly been qualitatively and subjectively driven for many decades.” — Rima Seiilova-Olson“Instead of these clunky tools, we give [healthcare providers] seamless tools that can analyze depression or anxiety in their patients.” — Rima Seiilova-Olson“I think the main impact that we, as founders, are excited about, is the emotional impact of our technology. Which is not quantifiable, but we believe it is going to be immense.” — Rima Seiilova-Olson“By connecting patients to access, we’re going to have a profound effect on society. [A society] that is observing skyrocketing trends in the rates of depression and anxiety, especially among young adults and adolescents.” — Rima Seiilova-Olson“We’re observing interesting trends where certain companies prioritize financial gains and revenue over clinical impact and the clinical outcomes for the patient. And those stories not only affect that one startup, it affects the whole industry.” — Rima Seiilova-Olson“I think every single AI startup in healthcare needs to prioritize the ethical implications of their product. So that as an industry, we cover some of the damage that has been done by some of our colleagues.” — Rima Seiilova-OlsonLinks:Rima Seiilova-Olson on LinkedInKintsugiKintsugi Journaling AppResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Creating a lasting impact on a large scale requires collaboration, and globally reducing emissions is one of the most impactful, large-scale goals one could have at this point in time.My guest on today’s show is Gavin McCormick, an economist by training who has immersed himself in the world of machine learning through the founding of WattTime, a company fighting climate change by automating devices to optimize energy in real-time in order to lower their carbon footprints. Currently, there are one billion devices making use of WattTime's technology, and in the next few years, Gavin hopes to increase that number to 30 billion!Tune in today to hear about the uncommon way that Gavin and his team approach their goals, the Climate TRACE project that has come about as a result of WattTime’s success, and why they don’t have any competitors! Key Points:Gavin’s educational background.The discovery that led Gavin to found WattTime.An explanation of what WattTime does.The motivation behind the founding of Climate TRACE.The role that machine learning plays at WattTime. The single objective function of WattTime.Methods that Gavin and his team use to ensure their actions are having the greatest impact.How the WattTime approach differs from the approach utilized by many other machine learning organizations.The importance of interdisciplinary collaboration.How WattTime measures their impact.One of WattTime’s major weaknesses.Advice for leaders of AI-powered organizations.What Gavin hopes WattTime will achieve in the next three to five years.  Quotes:“Renewable energy could have more impact if it could be cited in just the right locations and run at just the right times.” — Gavin McCormick“You can really substantially reduce the carbon footprint of a power grid by shifting all of the load to moments when there's surplus clean energy instead of just random times.” — Gavin McCormick“In any case where another organization, be they non-profit, university, for-profit, whatever, is rowing in a direction that is consistent with our mission and frankly doing it well, we would rather not spend the time and resources trying to recreate them or beat them. We would rather help them out.” — Gavin McCormick“If you're really serious about impact, then someone else's success is not a threat to you. It's a benefit.” — Gavin McCormickLinks Mentioned in Today’s Episode:Gavin McCormick on LinkedInWattTimeClimate TRACEResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Giving generic drugs a new life in oncology is a game-changing strategy for developing new and affordable treatment options for cancer patients. But it would take years to review the thousands upon thousands of published research studies on non-cancer drugs tested as cancer treatments to identify the most promising candidates. Luckily, nonprofit health tech startup Reboot Rx is stepping in to solve this problem! I spoke to Founder and CEO, Laura Kleiman about how her company is fast-tracking the development of affordable cancer treatments using AI technology. Working with a team of biomedical and clinical scientists, Reboot Rx uses machine learning and natural language processing to analyze large volumes of scientific literature, identify the most viable drugs, and develop pathways to generate definitive evidence and change the standard of care so that patients can benefit from them. In this episode, you’ll learn more about Reboot Rx’s multi-pronged approach and the challenges that come with processing such large volumes of data. Plus Laura shares her advice for tech leaders looking to solve problems that have a meaningful societal impact.Key Points:A look at Laura’s background and the personal story of what led her to create Reboot Rx.The important work Reboot Rx does to repurpose generic drugs for the treatment of cancer.An example that illustrates the role that machine learning plays in this process.Challenges that come with analyzing this type of data.How Reboot Rx’s machine learning developers collaborate with healthcare experts to ensure that their models are effective.Advice for leaders of AI-powered startups and nonprofits: choose problems that matter!How Reboot Rx is working to make their AI technology scalable.Quotes:“Patients need both more effective but also more affordable treatment options. Reboot Rx is giving generic drugs a new life in oncology, taking drugs that are already available to treat other non-cancer indications and repurposing them for the treatment of cancer.”“We use large language models to be able to process [large volumes] of scientific literature and predict which of these 600,000 studies are most likely to be relevant and extract key information about each of these studies.”“There's so much opportunity for the use of AI and machine learning right now. I would encourage other leaders in the space to choose problems to solve that can have a meaningful societal impact.”Links:Reboot RxLaura Kleiman on LinkedInLaura Kleiman on TwitterResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Today I'm joined by the Founder and CEO of Aquabyte, Bryton Shang, to discuss his mission to improve and enable fish farm efficiency and sustainability. Bryton fills us in on the role machine learning plays in monitoring underwater fish farm environments, the challenges of gathering and annotating data to build and train their models, and how their human-in-the-loop QA process converges to find solutions. Tune in to discover how Aquabyte’s mission-oriented, multidisciplinary, and multimodal nature impacts recruitment, and hear Bryton’s astute recruitment advice for leaders in the field. Aquabyte is a stellar example of an AI-powered startup looking to create a better, more sustainable future for the world at large.Key Points:Bryson Shang’s background and what led him to create Aquabyte.Aquabyte’s mission to enable efficient and sustainable fish farming.The role machine learning plays in monitoring underwater fish farm environments.How Aquabyte built their ML models.The practical challenges of training their models and their solution-finding systems.How Aquabyte’s mission-oriented, multidisciplinary, and multimodal nature impacts recruitment.Bryton’s recruitment advice for other leaders of AI-powered startups.His vision for Aquabyte’s impact in the next three to five years.Quotes:“[At] Aquabyte, we're focused on how machine learning and computer vision can help fish farmers be more efficient and sustainable.”“There’s definitely a mission-oriented aspect to [Aquabyte], which is attractive to a lot of folks that are looking for more of a mission-oriented bent.”“AI is a broad label, and I think the business domain in which you apply AI and how you apply it is really important, ultimately, to the success.”“By having autonomous fish farms, or even on land where you can have much more scalability of fish farming, then you can really increase the supply of fish.”Links:AquabyteBryton Shang on LinkedInNow Go Build with Werner Vogels – S1E3 BergenResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

All women face the risk of breast cancer, but early detection can greatly increase the chances of a positive outcome and reduce the need for aggressive treatment options. In this episode, I talk with Tobias Rijken, CTO and co-founder of Kheiron Medical Technologies, about leveraging AI for detecting breast cancer. We discuss the role of AI in improving medical care, the power of vertical integration and feedback loops, and what makes Kheiron different from other AI startups. Hear about the challenges of acquiring reliable data, whether using generative models is beneficial, details about the products Kheiron has created, and much more!Key Points:Tobias's professional background and why he created Kheiron Medical Technologies.Learn about the amazing work Kheiron Medical Technologies does and why it is important.Overview of why detecting breast cancer early is so vital and the challenges of screening.How AI can help resolve the current challenges in cancer screening.He explains the machine learning process and training the model used.The complications encountered in working with radiology images.Find out why image quality is key to the machine learning process.How he is able to account for the variation of technology and methods used.Outline of the regulatory process and how it impacts machine learning model development.Hear advice Tobias has for other leaders of AI-powered startups.Details about how Tobias approaches improving the models over time.Tobias tells us what Kheiron Medical Technologies has planned for the future.Quotes:“What I liked so much about machine learning is the ability it has to solve real-world problems. And in my opinion, real-world machine learning is very different from academic machine learning.”“Either the right information isn't available, or it is inaccurate, or there's missing information. We see AI as a tool to help address those information problems.”“The challenge when you sample uniformly from your whole dataset is that there will be cases you've sampled, where you may not have ground truth.”“For me, when I started this company, this was not about building a great model that has a great performance on a test dataset. This is about getting AI into the real world.”Links:Tobias Rijken on LinkedInTobias Rijken on TwitterKheiron Medical TechnologiesResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.