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NEJM AI Grand Rounds

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NEJM AI Grand Rounds, hosted by Arjun (Raj) Manrai, Ph.D. and Andrew Beam, Ph.D., features informal conversations with a variety of unique experts exploring the deep issues at the intersection of artificial intelligence, machine learning, and medicine. You’ll learn how AI will change clinical practice and healthcare, how it will impact the patient experience, and about the people who are pushing for innovation. Whether you are an AI researcher or a practicing clinician, these conversations will enlighten and surprise you as we journey through this very exciting field. Produced by NEJM Group.
20 Episodes
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In this episode of the AI Grand Rounds podcast, Dr. Adam Rodman shares his unique journey from a historian to a physician deeply interested in the intersection of medicine and artificial intelligence. He highlights his unconventional path, driven by an obsession with epistemology and nosology, and his early exposure to AI through historical references and personal experiences with language models. Rodman discusses the evolution of clinical reasoning, the importance of probabilistic models, the implications of AI in diagnostic processes, and details his work with large language models like GPT-4. He also reflects on the balance between the benefits and challenges of AI in medicine, emphasizing the necessity of collaboration between computer scientists and medical professionals. Throughout the episode, Rodman underscores the potential of AI to re-humanize medicine while cautioning against misapplications of the technology. Transcript.
In this episode of the NEJM AI Grand Rounds podcast, Dr. Nigam Shah, a distinguished Professor of Medicine at Stanford University and inaugural Chief Data Scientist for Stanford Health Care, shares his journey from training as a doctor in India to becoming a leading figure in biomedical informatics in the United States. He discusses the transformative impact of computational tools in understanding complex biological systems and the pivotal role of AI in advancing health care delivery, particularly in improving efficiency and addressing systemic challenges. Dr. Shah emphasizes the importance of real-world integration of AI into clinical settings, advocating for a balanced approach that considers both technological capabilities and the systemic considerations of AI in medicine. The conversation also explores the democratization of medical knowledge, why open-source models are under-researched in medicine, and the crucial role of data quality in training AI systems. Transcript.
In this episode of the AI Grand Rounds podcast, Dr. Daphne Koller charts her professional trajectory, tracing her early fascination with computers to her influential role in AI and health care. Initially intrigued by the capacity of computers for decision-making based on theoretical principles, Koller witnessed her niche area — once considered peripheral to AI — grow to dominate the field. Her curiosity led her from abstract theory to practical machine learning applications and eventually to the complex world of biomedicine. Throughout the podcast, Koller shares her shift from pure computer science to the integration of machine learning in biological and medical research. She explains the unique challenges of applying AI to biology, distinguishing it from more deterministic fields, and how these complexities feed into her work at insitro, where she is leveraging AI throughout the drug discovery and development process, from disease understanding to therapeutic application and monitoring. She advocates for the democratizing potential of AI, underscoring its capacity to enable broader participation in scientific inquiry and problem-solving.   Transcript.
In this episode of the AI Grand Rounds podcast, Dr. Eric Horvitz describes his career evolution from an interest in neurobiology to significant contributions in AI, particularly in understanding complex systems and applying AI in medicine. He discusses the shift from studying neurobiology to embracing AI and computational methods as tools for unraveling the complexities of the human mind and broader decision-making processes. Horvitz emphasizes the importance of probabilistic models and decision theory in AI, highlighting his work on bounded rationality and the challenges of interpretability in AI systems. He also reflects on the potential of AI in medicine, the necessity of responsible AI development, and the future of AI research. He suggests a blend of excitement and caution as AI technologies become increasingly integrated into various aspects of human life and decision making.   Transcript.
In this episode of the AI Grand Rounds podcast, Dr. James Zou shares his personal journey to discovering machine learning during his graduate studies at Harvard. Fascinated by the potential of AI and its application to genomics and medicine, Dr. Zou embarked on a journey that took him from journalism to the forefront of AI research. He has been instrumental at Stanford in translating machine learning advancements into clinical settings, particularly through genomics. The discussion also delves into the unique use of social media for gathering medical data, showcasing an innovative approach to AI model training with real-world medical discussions. Dr. Zou touches on the ethical implications of AI, the importance of responsible AI development, and the potential of language models like GPT-4 in medicine, despite the challenges of model drift and alignment with human preferences.   Transcript.
In this episode of the AI Grand Rounds podcast, Dr. Roxana Daneshjou shares her journey from a childhood influenced by early exposure to science to her current role as an assistant professor at Stanford. Her path includes a critical shift during medical school, where her interest in computational methods and human genomics led her to pursue both an M.D. and a Ph.D. Her specialization in dermatology was driven by its visual nature and the opportunity to form long-term relationships with patients. Dr. Daneshjou emphasizes the importance of AI in addressing fairness and bias in dermatology, discussing her research on disparities in AI performance across diverse skin tones. The podcast also delves into broader issues of AI in health care, discussing the potential and challenges of integrating large language models into medical practice, and highlighting the need for interdisciplinary collaboration between clinicians and computer scientists in AI development. Dr. Daneshjou’s optimism for the future centers on the new generation of medical professionals who are increasingly concerned about fairness and equity in AI.    Transcript.  
In this episode, Dr. Zak Kohane shares his journey into AI and medicine, reflecting on early influences from science fiction authors and programming experiences in his youth. He discusses his academic path, moving from programming and machine instruction to medical school, driven partly by practical advice and personal ambition. Kohane highlights his realization during medical school that medicine was not as scientifically advanced as he expected, motivating his interest in improving medical decision-making through AI. He recalls his time at MIT, contrasting the intellectual freedom there with today’s academic environment, and reflects on the impact of large language models in medicine, emphasizing their real-world applications and potential to transform medical practice. Kohane also discusses the importance of mentorship, his approach to nurturing talent, and the role of his department at Harvard in advancing the field of biomedical informatics. Finally, he shares insights on the NEJM AI journal, its objectives, and the challenges and opportunities in medical AI today.   Transcript.
In this episode, Dr. Judy Wawira Gichoya, Associate Professor in the Department of Radiology and Imaging Sciences at Emory University School of Medicine, details her journey from Kenya to the United States, from interventional radiology to artificial intelligence. Transcript. 
In this episode, vanguard geneticist Dr. George Church recounts his storied career from his early fascination with computers and science to his pioneering work in genomics and synthetic biology. Dr. Church developed innovative DNA sequencing methods that enabled the first sequencing of the human genome. He was also instrumental in developing CRISPR gene editing technology. Dr. Church discusses his controversial ideas around resurrecting wooly mammoths and using genome sequencing in dating apps to prevent genetic diseases. He also provides insights into founding genomics companies and the role of AI in advancing biotechnology. As a professor of genetics at Harvard Medical School and the founder of multiple genomics companies, Dr. Church emphasizes the potential for synthetic biology and genetics to transform medicine and society. Transcript.
In this episode, we’re joined by business magnate and investor Mark Cuban. Cuban describes his journey from humble beginnings as a self-taught programmer to becoming a successful serial entrepreneur and owner of an NBA team. Cuban shares the founding story of his pharmacy benefits company, Cost Plus Drugs, and his vision for reimagining the reimbursement system for care. He also articulates why he believes AI will be more transformative than prior technology shifts such as mobile and the internet and unpacks the potential of AI in addressing previously intractable problems in healthcare. Cuban is a self-made billionaire, bestselling author, star of Shark Tank, and owner of the Dallas Mavericks. Transcript
In this episode, pioneering informatician Dr. Atul Butte guides listeners through his storied career, from his early days as a pediatric endocrinologist and informatician in Boston to his trailblazing work on the West Coast. Dr. Butte describes his trailblazing efforts to both harness large-scale public biomedical data and share patient data across the entire UC Health System. Dr. Butte also discusses his entrepreneurial journey, including the genesis and vision behind companies like NuMedii. Dr. Butte is the Priscilla Chan and Mark Zuckerberg Distinguished Professor at UCSF and the Chief Data Scientist of the UC Health System.   Transcript
In this thought-provoking episode, Dr. Ziad Obermeyer delves into the complex issues of bias, safety, and generalizability of medical AI. Dr. Obermeyer emphasizes the importance of machine learning researchers’ task formulation, an often-overlooked yet significant determinant of bias in AI algorithms. Highlighting the dual impact of machine learning, he compares two of his works that demonstrate how AI can either exacerbate or help mitigate health care disparities. Lastly, he discusses the significant challenges encountered in the development of AI models due to siloed and inaccessible data, sharing his own experiences and solutions in tackling this issue. Dr. Obermeyer is the Blue Cross of California Distinguished Professor at the Berkeley School of Public Health, Co-Founder of Nightingale Open Science, and Co-Founder of Dandelion Health. Transcript    
Dr. Marzyeh Ghassemi has been at the forefront of medical machine learning for several years. In this episode, she describes her group’s work and her perspectives on developing and applying machine learning to understand and improve health in ways that are robust, private, and fair. Dr. Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science and the Institute for Medical Engineering & Science. Transcript
Large language models (LLMs) like ChatGPT have proven highly capable of a broad array of natural language tasks including summarizing text, generating prose, and answering questions. This episode’s two guests, Dr. Alan Karthikesalingam and Vivek Natarajan of Google, describe their team’s recent efforts to adapt and evaluate LLMs for clinical applications. Alan and Vivek took very different paths to becoming leading medical AI researchers and in this episode they also share their educational journeys and perspectives on where the field is headed. Alan is a Senior Staff Clinician Scientist and Research Lead at Google Health and Vivek is a Research Scientist at Google Health AI. Transcript
Dr. Michael Abramoff is a renowned ophthalmologist and medical AI pioneer. In this episode, we explore his groundbreaking work that led to the first FDA-authorized device that does not require a physician, IDx-DR, which detects more than mild diabetic retinopathy from digital images of the eye. Dr. Abramoff also reflects on the challenges of commercialization, AI reimbursement, and the ethical imperatives for AI in health care centered around patient benefit. Dr. Abramoff is the Robert C. Watzke Professor of Ophthalmology and Visual Sciences at the University of Iowa and Founder & Executive Chairman of Digital Diagnostics, an autonomous AI diagnostics company that developed IDx-DR. Transcript
Dr. Peter Lee has shaped computer science from academia, government, and industry. He has chaired a major computer science department, built a new technology office at DARPA, and now serves as Corporate Vice President at Microsoft, where he leads Microsoft Research and its nine worldwide laboratories. In this episode, Peter reveals Microsoft’s interest in health care and the origins of the OpenAI and Microsoft partnership, and he speculates on how large language models like ChatGPT will transform medicine. Transcript
Dr. Lily Peng has driven major medical AI efforts along the long and arduous path from ideation to deployment. From publishing a landmark study in 2016 presenting an AI model to detect diabetic retinopathy in retinal fundus photographs to evaluating deep learning systems in India and Thailand, she has a unique and wide-ranging perspective on both model development and real-world validation. She continues to lead medical AI efforts as a physician-scientist and the Director of Product Management at Verily. Transcript
Dr. Pranav Rajpurkar has been at the forefront of medical AI for his entire career. As a graduate student in computer science at Stanford, he created some of the first AI models for radiology and created a suite of datasets and benchmarks that have been widely used by researchers across the world. Now, as a faculty member at Harvard, his group has continued to push the frontier of medical AI across many different specialties including radiology, pathology, and cardiology. Transcript
Dr. Euan Ashley is a pioneer. In 2010, he led the team that conducted the first clinical interpretation of a human genome, and he holds the record for the world’s fastest genomic diagnosis. He even has a Guinness World Record to prove it. In this wide-ranging discussion, Dr. Ashley shares the stories behind these feats, his experiences applying artificial intelligence to genomics and to cardiology, and his views on whether and how AI will change medicine. Transcript
[Transcript] How will AI change clinical practice and healthcare, and how will it impact the patient experience? Who are the people pushing for change and what are their goals? These are just a few of the topics that we hope to explore on NEJM AI Grand Rounds, a new podcast from NEJM Group. This podcast will be an informal conversation with a variety of unique experts exploring the deep issues at the intersection of artificial intelligence and medicine. Whether you are an AI researcher or a practicing clinician, we hope these conversations will enlighten and surprise you as we journey through this very exciting field. Hi, I’m Raj Manrai. I’m an assistant professor of Biomedical Informatics at Harvard Medical School and a co-host of NEJM AI Grand Rounds. I’m fascinated by both the growing abilities of AI and by medical decision making.  I am excited about an AI enabled future in medicine that rests on strong clinical evidence. I’m Andy Beam, an assistant professor of epidemiology at the Harvard School of Public Health and cohost. I’ve been interested in AI for as long as I can remember, and I’m so excited that we're starting to see real progress on important medical problems. NEJM AI Grand Rounds will be launching in mid-December, and we hope you are as excited as we are to have this important discussion. Follow us now on Apple, Spotify, Google, or wherever you find your podcasts.  
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