NEJM AI Grand Rounds NEJM Group

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.
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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.
 
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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.
 
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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.
 
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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. 
 
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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.
 
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