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Medical expertise has always been scarce. Dr. Karan Singal believes AI can help change that. Drawing on his work at OpenAI and earlier efforts behind MedâPaLM, he discusses how clinicians and patients are already using AI to answer questions, support decisions, and navigate care. He argues that the future of health AI is not only about improving model performance, but also about helping people advocate for themselves more effectively. Through HealthBench and ChatGPT for Clinicians, his team is exploring how to make these systems safer, more useful, and more trustworthy. The result is a vision of health care where expertise becomes more accessible without losing sight of clinical responsibility.
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Dr. Travis Zack, Chief Medical Officer of OpenEvidence, takes us behind the scenes of the start and growth of the company, and brings a clinicianâs perspective to one of medicineâs hardest questions: how should artificial intelligence support decision-making? In this episode, he emphasizes that reasoningânot just correctnessâdefines good care, and that evidence must be contextual, accessible, and usable. He explores how physicians use AI to reduce uncertainty, why global constraints challenge the idea of a single âright answer,â and how trust depends on transparent use of medical literature. For clinicians navigating complex decisions, this conversation highlights both the promise and the limits of AIâand the enduring importance of human judgment.
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Doctronic CMO Dr. Byron Crowe describes how administrative complexity can interfere with timely, effective treatment, and how AI may help address those challenges. Crowe discusses Doctronicâs use of autonomous AI to renew prescriptions, arguing that this application can streamline care while maintaining clinical oversight. For physicians, this shift raises important questions about workflow, responsibility, and patient engagement. Crowe emphasizes that the goal is not automation for its own sake, but more reliable and accessible care. As these tools evolve, their impact will depend on how thoughtfully they are integrated into clinical practice.
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Dr. Kyunghyun Cho is a leading AI researcher best known for co-authoring a landmark 2014 paper that introduced neural machine translation. In this episode, he discusses his wide-ranging career spanning fundamental AI research, co-founding Prescient Design (acquired by Genentech), and driving applications of AI in health care. For clinicians, Choâs core message is pragmatic: AI should help health care run better. After years of work at NYU Langone, he reframed AI in medicine from solving rare diagnostic puzzles to improving operational prediction at scale. Cho emphasizes purposeâbuilt data, careful fineâtuning, and regulatory accountability. His perspective connects technical rigor with system stewardshipâand insists that patient voices must be present in AI governance.
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Clinical AI only helps patients if clinicians and health systems trust it. Seth Hain describes how Epic is building foundation models that respect institutional autonomy, minimize burden, and prioritize safety. He discusses scaling laws in structured medical data, cautious deployment for clinical interventions, and why understanding causalityânot just correlationâis essential. This conversation reframes AI not as disruption, but as infrastructure for safer, more reliable care.
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For Dr. Marinka Zitnik, the promise of AI in medicine begins with acknowledging the scale of the problem. Most patients with rare diseases have no approved treatments, and traditional drug development timelines make progress painfully slow. In this conversation, she describes how AI-driven drug repurposing offers a way to work within existing constraints while still opening new therapeutic possibilities.
She also highlights a structural issue that has limited impact: machine learning and biology communities often work in parallel, not together. By building shared benchmarks and collaborative spaces, Marinka argues, researchers can focus models on problems that truly matter for patients.
The episode introduces her definition of AI agents as systems that can take actions and learn from outcomes â a capability she sees as essential for scientific discovery beyond static prediction. Throughout the discussion, Marinka returns to the value of academic freedom: the ability to chase difficult questions that require long time horizons and interdisciplinary thinking.
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For Dr. Zak Kohane, this yearâs advances in AI werenât abstract. They were personal, practical, and deeply tied to care. After decades studying clinical data and diagnostic uncertainty, he finds himself building his own EHR, reviewing his childâs imaging with AI, and re-thinking the balance between incidental and missed findings. Across each story is the same insight: clinicians and machines make mistakes for different reasons â and understanding those differences is essential for safe deployment.
In this episode, Zak also highlights where AI is spreading fastest, and why: reimbursement. While dermatology and radiology arenât broadly using AI for interpretation, revenue-cycle optimization is advancing rapidly. Meanwhile, ambient documentation has exploded â not because it increases accuracy or throughput, but because it improves clinician satisfaction in strained systems.
Yet the most profound theme, he argues, is values. Models already show implicit preferences: some conservative, some aggressive. And unlike human clinicians, no regulatory framework examines how those preferences form. Zak calls for a new form of oversight that centers patients, recognizes bias, and bridges clinical expertise with technical transparency.
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As a cognitive psychologist, Dr. Laura Zwaan studies how humans makeâand learn fromâmistakes. In this episode of NEJM AI Grand Rounds, she brings that lens to AI, showing how machines inherit our biases and why both need transparency and reflection. From the challenge of defining diagnostic error to the promise of âmachine psychology,â Dr. Zwaan explores how human reasoning can inform safer algorithms and better care. Her message is clear: the path to trustworthy AI begins with understanding ourselves.
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In this episode, Dr. Jonathan Chen joins the hosts to discuss his path from teenage programmer to Stanford physician-informatician and why machine learning has both thrilled and unnerved him. From his 2017 NEJM essay warning about âinflated expectationsâ to his latest studies showing GPTâ4 outperforming doctors on diagnostic tasks, Dr. Chen describes a discipline learning humility at machine speed. This conversation spans medical education, automation anxiety, magic, and why empathyânot memorizationâmay become the most valuable clinical skill.
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Dr. Karandeep Singh brings two worlds together: programming and medicine. In this conversation, he explains how early experiments with code led him to biomedical informatics, why gaps between paper performance and clinical reality must be confronted, and how governance committees weigh ethics and safety. Now serving as Chief Health AI Officer at UC San Diego Health, he reflects on lessons from deploying sepsis prediction tools, the risks of hype, and the promise of integration. For clinicians, Singhâs story is a reminder that the best AI is guided by patient care, deep expertise, and humility about the limits of technology.
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Dr. Jeremy Friese knows medicine from both sides. A practicing radiologist and technology executive, heâs seen firsthand how administrative burden undermines care. In this episode of NEJM AI Grand Rounds, he walks through the origins of prior authorization, explains why he believes artificial intelligence can close the gap between patients and payers, and argues that real reform means showing your workâjust like in math class. At Humata, heâs combining human oversight, LLMs, and interoperability to try to fix a broken system. For clinicians overwhelmed by back-office complexity, this conversation offers both urgency and optimism.
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Dr. Andy Beam has trained models, mentored scientists, and used data to quantify the value of treatments. In this episode of NEJM AI Grand Rounds, Raj Manrai turns the table on his co-host, reflecting on how Andyâs childhood misdiagnosis, and the failure of human recall, revealed the diagnostic promise of machine learning. As a Harvard professor, he mentored hybrid thinkers and built tools to evaluate safety, not just performance. Now CTO of Lila Sciences, heâs building an experimental AI system to generate its own hypotheses and test them in the real world. This conversation is a front-row seat to the next evolution of science.
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In this episode of NEJM AI Grand Rounds, guests Drs. Alan Karthikesalingam and Anil Palepu of Google walk co-hosts Raj Manrai and Andy Beam through the making and evaluation of AMIE, an AI system designed to conduct clinical conversations with patients. Alan and Anil explain how AMIE was trained using synthetic doctor-patient interactions generated by LLMs playing multiple rolesâdoctor, patient, critic, and moderator. They reveal how synthetic dialogue, guided by structured feedback and grounded in search, proved more effective than noisy real-world transcripts in building a model that could reason, ask questions, and show empathy. The discussion also covers what the âlong tailâ of medicine demands for building robust AI systems and how AMIE might one day augment real clinical workflows. Finally, Alan reflects on how AI has changedâand how it hasnâtâin the two years since he was last on the podcast.
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Dr. Shiv Rao, cardiologist and CEO of Abridge, joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds for an inspiring conversation at the intersection of medicine, technology, and meaning. Shiv shares the origin story of Abridge, reflecting on how a deeply human encounter in clinic sparked the idea for a company now transforming clinical documentation across more than 100 health systems. From his early days programming electronic music to navigating LLM deployment at scale, Shiv offers a rare look into the soul of a founder building not just infrastructure â but a movement. He unpacks how generative AI can be used to restore presence in the clinic, what it takes to earn clinician trust, and why he believes taste, empathy, and curiosity are the real moats in health care AI.
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Dr. Faisal Mahmood, Associate Professor of Pathology at Brigham and Womenâs Hospital and Harvard Medical School, joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds to explore the frontier of computational pathology. From pioneering foundational models for whole slide imaging to commercializing a multimodal generative AI copilot for pathology, Faisal shares how his team is redefining whatâs possible in digital diagnostics. He discusses the power of open-source culture in accelerating innovation, his labâs FDA breakthrough designation, and how generative AI could trigger widespread digitization in pathology. Faisal also reflects on his creative approach to problem selection and offers a vision for a future shaped by patient-level foundation models and agent-led computational biology.
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Morgan Cheatham joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds to discuss the evolving landscape of artificial intelligence in health care, from its role in automating clinical documentation to its transformative potential in genomic medicine. A venture capitalist and future physician, Morgan shares how his background in computational decision sciences led him to medical school and investing, offering insights into how AI is reshaping everything from disease phenotyping and clinical decision-making to scaling precision medicine. He reflects on his work evaluating ChatGPTâs performance on the USMLE, the growing importance of genomic learning health systems, and why the biggest challenge isnât technological innovationâbut aligning payment models to support AI-driven advancements in medicine.
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Dr. Emily Alsentzer joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds to discuss the evolution of natural language processing (NLP) in medicine. A Stanford faculty member and expert in clinical AI, Emily shares her journey from pre-med to biomedical AI, the role of language models in medical decision-making, and the ethical considerations surrounding bias in AI. The conversation explores everything from the early days of rule-based NLP to the modern era of large language models, the challenges of evaluating AI in clinical settings, and what the future holds for open-source medical AI.
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In this return appearance on NEJM AI Grand Rounds, Dr. Zak Kohane joins hosts Raj Manrai and Andy Beam to discuss the evolving landscape of AI in medicine. As the first repeat guest on the show, Dr. Kohane shares insights on health care system challenges, the Human Values Project, and his perspectives on the most significant AI developments of 2024. The conversation explores everything from the practical applications of AI in health care to philosophical discussions about machine psychology and the future of doctor-patient relationships.
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In this episode of NEJM AI Grand Rounds, hosts Raj Manrai and Andy Beam interview Larry Summers about artificial intelligenceâs transformative potential and its implications for society. The conversation explores Summersâ perspective on AI as potentially the most significant technology ever invented, his role on OpenAIâs 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 AIâs 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 sonâs 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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