In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Larry Summers about artificial intelligence’s transformative potential and its implications for society. The conversation explores Summers’ perspective on AI as potentially the most significant technology ever invented, his role on OpenAI’s board following the November 2023 leadership transition, and his thoughts on how AI will reshape economics and human society. The episode provides unique insights into AI’s development trajectory, the challenges of technological prediction, and the intersection of economics and artificial intelligence.

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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Courtney Hofmann, a mother whose use of ChatGPT led to her son’s diagnosis of tethered cord syndrome after seeing 17 doctors over three years, and Dr. Holly Gilmer, the pediatric neurosurgeon who confirmed and treated the condition. The conversation explores how AI helped bridge diagnostic gaps, systemic health care challenges that led to missed diagnoses, and the evolving role of AI in patient advocacy and medical practice. The episode highlights the importance of combining AI insights with human medical expertise, while discussing both the potential and limitations of AI in health care.

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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Dr. David Ouyang, a cardiologist and AI researcher at Cedars-Sinai Medical Center. The conversation explores Ouyang’s journey from medical training to AI research and entrepreneurship, his groundbreaking work in applying AI to cardiology imaging, and the challenges of bringing AI innovations from academia to clinical practice. Ouyang discusses his experience conducting randomized controlled trials (RCTs) for AI algorithms in echocardiography, the process of commercializing research through Y Combinator, and the hurdles in reimbursement for AI-based medical devices. The episode also delves into the future of AI in cardiology, the importance of clinician involvement in AI development, and the potential impact of large language models (LLMs) on medical practice. Ouyang shares insights on balancing clinical value with business considerations in health care AI and offers advice for researchers looking to conduct clinical trials for AI technologies.

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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Dr. Noa Dagan and Dr. Ran Balicer from the Clalit Research Institute in Israel. The conversation explores Clalit’s groundbreaking work in implementing predictive models at the point of care, their contributions to COVID-19 research, and the potential of AI in revolutionizing public health. Dagan and Balicer discuss the unique data set spanning more than half of Israel’s population, their approach to integrating AI into clinical practice, and their vision for the future of data-driven health care.

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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Dr. Vijay Pande, a general partner at Andreessen Horowitz (A16Z) where he leads investments in health care and life sciences. The conversation explores Pande’s journey from academia to venture capital, his views on the future of AI in health care and biomedicine, and insights into the investment landscape for biotech and health tech companies. Pande discusses the challenges and opportunities in integrating AI into medical practice, the potential for AI to democratize health care access, and his thoughts on the development of artificial general intelligence (AGI).

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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Dr. Rohaid Ali and Dr. Fatima Mirza, a married couple and chief residents at Brown University. The conversation explores their innovative work applying AI to health care, focusing on two major projects:

1. Using ChatGPT to simplify surgical consent forms, making them more accessible to patients. This initiative led to widespread adoption within their healthcare system and inspired similar changes in other medical documentation, and

They share insights on balancing personal and professional collaboration as a married couple working on research together. The episode features a lighthearted “newlywed game” segment, testing how well the couple knows each other’s perspectives. It concludes with Ali and Mirza offering advice to early-career doctors interested in AI and sharing their vision for AI’s future in medicine, highlighting the importance of ensuring equitable access to these technologies and the need for thoughtful implementation by medical professionals.

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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.

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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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