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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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 — 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 Women’s 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 what’s possible in digital diagnostics. He discusses the power of open-source culture in accelerating innovation, his lab’s 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 ChatGPT’s performance on the USMLE, the growing importance of genomic learning health systems, and why the biggest challenge isn’t technological innovation—but 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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