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, Cho’s 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 purpose‑built data, careful fine‑tuning, and regulatory accountability. His perspective connects technical rigor with system stewardship—and 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 causality—not just correlation—is 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 — 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 year’s advances in AI weren’t 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 child’s 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 — 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 aren’t broadly using AI for interpretation, revenue-cycle optimization is advancing rapidly. Meanwhile, ambient documentation has exploded — 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 make—and learn from—mistakes. 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 “machine psychology,” 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 “inflated expectations” to his latest studies showing GPT‑4 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 empathy—not memorization—may 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, Singh’s 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, he’s 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 work—just like in math class. At Humata, he’s 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 Andy’s 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, he’s 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 roles—doctor, 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 “long tail” 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 changed—and how it hasn’t—in the two years since he was last on the podcast.

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