Long-form conversations on healthcare AI, including ARISE episodes, Computational Medicine colloquia, and selected interviews and guest podcast appearances.
In this episode, JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, speaks with David Wu, MD, PhD, and Adam Rodman, MD, MPH, about what safe clinical use of LLMs requires. Drawing on the framework of Do No Harm, they examine failure modes, limits of accuracy-based evaluation, clinician AI interaction, and safeguards needed as medical AI moves into patient care.
Alex is a Partner at Khosla Ventures with a focus in biotechnology, healthcare, data science, and AI/ML. He works on new investments and sits on the boards of many KV portfolio companies. Alex’s education and training encompasses physics, biology, biomedical informatics, and medicine; he also held a postdoctoral fellowship in biochemistry and genomics. As a scientist, he has published more than 50 scientific articles (h-index 38), primarily at the intersection of computer science, biology, and healthcare. As an inventor, he has licensed IP to three companies. As an entrepreneur, he has been a co-founder or early employee at a range of startups. He is committed to increasing access to high quality healthcare, STEM education and entrepreneurship opportunities for the underserved and under-represented. Early in his career Alex taught himself programming and joined the MITRE Corporation as an artificial intelligence research engineer. Subsequently, Alex went to graduate school at Stanford in biomedical informatics, where he worked with genomics data on applications for biomarker discovery and therapeutics development. At Stanford, he also initiated early clinical trials for digital health using connected devices including health wearables and AR/VR technologies. Since 2015, Alex has been at KV working on evaluating deal flow and new investments, supporting portfolio companies, and company incubation/creation. Alex holds an undergraduate degree in physics from Brandeis University and an MS in biology from Tufts University. He also holds an MS and Ph.D. in biomedical informatics as well an M.D. from Stanford University. Abstract: This talk will discuss a variety of things to consider when contemplating the leap from academia into entrepreneurship.
An intensive four-week hybrid learning experience designed to equip healthcare professionals with the strategic insight and practical tools needed to responsibly implement and scale AI solutions in clinical and organizational settings. The program blends asynchronous online learning, recorded live virtual sessions, and a two-day in-person immersion at Stanford University. Participants will engage with Stanford faculty and industry leaders, explore real-world case studies, and develop strategic approaches to integrating AI in healthcare delivery. Beyond the in-person experience, participants will deepen their learning through virtual discussions, applied exercises, and expert-led sessions that provide both conceptual understanding and practical frameworks. The program is intentionally structured to support immediate application to participants’ professional contexts. By the end of the program, participants will walk away with awareness of best practices in healthcare AI implementation, practical frameworks for AI evaluation, and a network of peers and thought leaders to support their ongoing work in advancing responsible AI in healthcare.
Dr. Theo Zanos is a Professor & AVP, and the head of the Division of Health AI at Northwell Health and the Neural and Data Science Lab at the Institute of Health System Science and Institute of Bioelectronic Medicine, at the Feinstein Institutes for Medical Research and the Zucker School of Medicine, Hofstra Northwell. He received his Engineering diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in Greece, his MSc and PhD in Biomedical Engineering from the University of Southern California and postdoctoral training at the Montreal Neurological Institute at McGill. His current research focuses on developing and applying AI/machine learning methods on multimodal healthcare, neural and physiological data to enable early diagnosis, disease severity assessment, and personalization and adaptability of therapies. He has been awarded multiple federal and industry grants, totaling more than $15M of external funding from NIH, CDC and other federal and industry sources, and published more than 70 peer-reviewed papers, in journals such as Nature Communications, Nature Machine Intelligence, PNAS, JAMA, npj Digital Medicine, Neuron (Cell Press) and others. He has been awarded twice the Northwell Excellence in Research Award, finalist in Fast Company’s World Changing Ideas in AI, twice finalist in Northwell’s Innovation Challenge, the Jean Timmins Award and the Center of Excellence in Commercialization and Research Award. Abstract: Artificial intelligence offers transformative potential in healthcare through predictive algorithms, preventive interventions, and personalized treatment approaches. However, successful implementation requires rigorous research validation, health system integration, and consideration of real-world performance dynamics. We will discuss the Division of Health AI at Northwell Health’s strategic framework organized around three pillars: Prevent, Predict, and Personalize, to improve patient outcomes and health system operations. I will present our work on point-of-care multimodal inhospital deterioration prediction models, operational nurse staffing forecasting solutions with tangible ROIs, and bioelectronic medicine ML applications using anatomical data and wearable sensors to enable precision treatment selection.
Bio: Dr. Khatri is a faculty member in Institute for Immunity, Transplantation and Infection (ITI) and the Division of Computational Medicine in Department of Medicine at Stanford University. His research focuses on the intersection of machine learning, computational immunology, and translational medicine with the overarching goal of accelerating translation of immune response-based diagnostics and therapies to clinical practice across a broad spectrum of inflammatory diseases, including infections, autoimmune diseases, organ transplant, cancers, and vaccines. His lab develops machine learning-based methods and computational frameworks to leverage biological, clinical, and technical heterogeneity across multiple datasets to identify robust disease signatures and identify novel therapies for inflammatory conditions.
Tim Sweeney, MD, PhD, is a graduate of the Stanford Biomedical Informatics (BMI) program (postdoctoral MS, 2015). He is cofounder and CEO of Inflammatix, a precision diagnostics company developing first-in-class tools for improving hospital care, including its lead product, Triverity. Dr. Sweeney has authored more than 100 peer-reviewed manuscripts and abstracts, served as principal investigator on 10 federally funded contracts from NIH, DoD, BARDA, and related agencies, and serves on the board of the STEPS Alliance.
Bio (Dr. Peter Brodeur): Dr. Peter Brodeur is a rising cardiology fellow at Harvard Medical School’s Beth Israel Deaconess Medical Center. Dr. Brodeur is an affiliate of ARISE, reviewer for NEJM AI, and former life sciences strategy consultant. His research focuses on human computer interaction and LLM clinical reasoning. Bio (Dr. Liam McCoy): Liam McCoy is a resident physician in neurology at the University of Alberta, and Research Affiliate at the Massachusetts Institute of Technology. He engages in research related to the effective, ethical integration of clinical reasoning AI systems in practical healthcare contexts.
Zinaida Good, Ph.D., is an Assistant Professor of Medicine in the Division of Immunology and Rheumatology and the Division of Computational Medicine at Stanford University. She also serves as the Director of the Stanford Center for Cancer Cell Therapy Data Hub. The goal of her research program is to understand and enhance engineered T cell immunotherapies for cancer and immune-mediated diseases through innovative computational approaches and systems immunology. Her lab leverages innovation in machine learning and clinical multiomic datasets to build artificial intelligence systems for advanced T cell therapy design. Dr. Good earned her Ph.D. in Computational & Systems Immunology from Stanford University. Her work includes 4 first-author papers (Nature Medicine 2018 & 2022, Nature Biotechnology 2019, Trends in Immunology 2019), 18+ co-authored papers (including Nature 2019, 2022, 2024, Science 2021, Nature Methods 2016, 2022, and NEJM 2024), and an initial senior author papers (ICML 2025, NeurIPS 2025, Frontiers in Immunology 2025). Her research is supported by the NIH/NCI Pathway to Independence Award, NIH/OD Multimodal AI Initiative, and the Weill Cancer Hub West. Dr. Good has been named an Arthur & Sandra Irving Cancer Immunology Fellow in 2022, Parker Bridge Fellow in 2023, and an AACR-Woman in Cancer Research Scholar in 2024.
Bio (Dr. Chen): Dr. Sharon F. Chen is an academic pediatric infectious diseases physician at Stanford University School of Medicine, involved in patient care, teaching and research. Dr. Chen has a special interest in viral infections affecting immunocompromised patients, and she collaborates with viral/immunology laboratories to conduct studies, primarily on T-cell responses. As Co-director of Stanford Children’s’ Pediatric Infectious Diseases Program for Immunocompromised Hosts (PIDPIC), Dr. Chen develops and conducts clinical studies to establish best practices and to start new clinical initiatives that push the frontier. Dr. Chen’s scholarly interests also extends to education research in how people think and make decisions. In collaboration with the learning sciences, she has created a problem-solving framework that reveals the hidden “thinking habits” needed for solving complex problems. An AI adaptation of the framework is being tested in clinical medicine to augment physicians as they make patient-care decisions. Bio (Dr. Ma): Dr. Flora Ma is a Clinical Assistant Professor at Stanford School of Medicine and a distinguished leader in geropsychology and clinical mental health. Currently serving on the Executive Leadership Committee of Stanford’s Faculty Senate, Dr. Ma leads funding, public positioning, and emerging technology discussions across the medical school. At Stanford, Dr. Ma provides both inpatient and outpatient psychological services through the ADAPT, Geriatric Psychiatry, and INSPIRE Clinics, specializing in complex cases involving dementia, psychotic-spectrum disorders, and personality disorders. Dr. Ma’s research focuses on technology-enhanced mental health interventions for older adults, with particular emphasis on Veterans’ care and cultural competency. She has published extensively in peer-reviewed journals and serves as Assistant Editor for the International Journal of Aging and Mental Health. Her leadership extends nationally as a Committee Chair in the American Psychological Association. Her research has culminated in her own AI that helps doctors and nurses with patient empathy training by practicing live, real-time Zoom conversations with AI patients. She is currently expanding this emotion-based conversation simulator across corporate HR, sales, and other high leverage environments. Bio (Dr. Oliveira): Renan Gianotto-Oliveira, MD, PhD, is an emergency physician and medical education researcher specializing in technology-enhanced assessment, including augmented reality and virtual simulation. He is a research assistant on the Clinical Mind AI project at Stanford University’s CHARIOT Lab and a faculty member at São Leopoldo Mandic Medical School (Brazil), working in the Assessment Center.
Dr. Laura Zwaan is an Associate Professor at the Institute of Medical Education Research Rotterdam (iMERR) at Erasmus MC, The Netherlands. Trained in cognitive psychology and epidemiology, she leads a research group dedicated to advancing understanding of diagnostic reasoning and diagnostic error, with a growing focus on human–AI collaboration in clinical decision-making. Her work combines a wide range of quantitative and qualitative methods to study how clinicians make diagnostic decisions and how these processes can be optimized to strengthen diagnostic safety and accuracy. Dr. Zwaan has received multiple research grants and awards, including the Mark Graber Award, in recognition of her contributions to improving diagnostic safety.
Bio Anton: Dr Anton Van Der Vegt is an Advanced QLD Industry Research Fellow with the Centre for Health Services Research at UQ Faculty of Medicine. Originally trained as a Mechanical Engineer and Computer Scientist, Anton has worked across Australia, Europe, the US designing, developing and implementing sophisticated software programs. Recently Anton architected and managed two projects within Queensland Health to support AI experimentation with health data, including the development of CLARA, a Clinical AI research Accelerator data lab. Currently Anton is collaborating with clinicians at Queensland health to develop and prospectively trial AI methods to predict sepsis and clinical deterioration. Bio Ian: Dr Ian Scott is consultant general physician and former Director of Internal Medicine and Clinical Epidemiology at Princess Alexandra Hospital in Brisbane. He is currently Clinical Consultant in AI at the Digital Health and Informatics Division of Metro South Hospital and Health Service, chairs the Metro South Clinical AI Working Group and Queensland Health Sepsis AI Working Group, and is Professor in Clinical Decision-making at the University of Queensland (UQ). He has co-authored multiple papers on the use of AI in healthcare, is principal investigator for several AI trials and has collaborations with colleagues within the UQ Digital Health Centre, the Centre for Health Informatics at Macquarie University, the CRC in Digital Health at Queensland University of Technology and the Clinical and Business Intelligence Unit of eHealth Queensland. He has longstanding research interests in clinical informatics, evidence-based medicine, clinical reasoning and quality and safety improvement.
Artificial intelligence is reshaping how clinicians think and care for patients. In our conversation with Jonathan Chen, MD, PhD, Associate Professor of Medicine and Biomedical Data Science at Stanford University, he shares how AI has enhanced his own clinical work and the practical steps that foster trust and adoption among clinicians. The discussion goes beyond technology to explore the emotional dimensions of care, address bias, and outline the safeguards needed to use AI responsibly. We also review AI’s impact on medical education and the evolving hospital landscape for responsible, future-ready AI-enabled care. Join us for a thoughtful exploration of the promise, challenges, and path forward to integrate AI into clinical decision making.
AI chatbots have advanced rapidly, incorporating new reasoning architectures that reshape decision-making and medical education. Jonathan Chen, MD, PhD, and Ethan Goh, MD, MS, of Stanford University join JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss the latest generation of AI models, the importance of evaluating benefits and harms, and sycophancy in AI systems.
How can AI systems reason safely in the open world of medicine? JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, talks with Eric Horvitz, MD, PhD, Chief Scientific Officer at Microsoft, about the future of AI in 5 years to 100 years, from neurons to the nebulous, and how we can guide AI to be copilots while maintaining integrity and safety in the clinical arena.
The 2025 AIMI Symposium was a hybrid conference presented by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) on June 3, 2025. Panelists: Moderated by Ethan Goh, MD, Executive Director, Stanford AI Research and Science Evaluation, Stanford University Emily Alsentzer, PhD, Assistant Professor of Biomedical Data Science and, by courtesy, Computer Science, Stanford Karan Singhal, MS, Health AI Lead, OpenAI Khaled Saab, PhD, Research Scientist, Google DeepMind Marinka Zitnik, PhD, Associate Professor of Biomedical Informatics, Harvard Medical School
Chatbots may have a role in enhancing clinical care, but the best way to apply them remains a work in progress. Jonathen Chen, MD, PhD, and Ethan Goh, MD, MS, of Stanford, join JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss their randomized clinical trial published in JAMA Network Open investigating the use of chatbots in clinical practice.
