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JAMA+ AI Conversations

JAMA Network
Artificial Intelligence
AI In Healthcare
Machine Learning
Clinical Trials
Mental Health
Healthcare
Generative AI
Patient Care
Health Disparities
Electronic Health Records
Cardiology
Patient Outcomes
Suicide Prevention
Healthcare Communication
Healthcare Data
Early Warning Scores
Patient Satisfaction
Digital Health Technology
Neuroscience
Medicine

Discover the future of medicine with JAMA+ AI Conversations. This collection of interviews with clinicians, researchers, and AI experts explores how AI is impacting medicine – from clinical practice to training and research. Join us to uncover what lies ahead at the intersection of AI and medicine.

PublishesTwice monthlyEpisodes75Founded2 years ago
Number of ListenersCategories
Health & FitnessScienceMedicine

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Artwork for JAMA+ AI Conversations

Latest Episodes

The rise in use and function of ambient AI scribes is arguably one of the fastest technologic changes ever seen in health care. In this episode of Healthy Dialogue, host Derek Angus, MD, MPH, is joined by Vincent Liu, MD, MS, Chief Data Officer of Th... more

In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Yulin Hswen, ScD, MPH, discuss Anthropic's participation in the Vatican presentation of Pope Leo XIV's AI encyclical, Magnifica Humanitas. The conversation explores AI ethics, interp... more

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Is it enough for AI to be accurate, or are we mistaking performance for impact? Does AI change clinician behavior, improve patient decisions, or simply create a convincing performance of intelligence? Yun Liu, PhD, research scientist at Google Resear... more

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Every patient has a story, but in modern health care that story is buried across thousands of notes, lab results, and fragmented records. Nigam H. Shah, MBBS, PhD, of Stanford University Department of Medicine joins JAMA Associate Editor Yulin Hswen,... more

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Recent Guests

Yun Liu
Senior Staff Research Scientist in Google Research
Google Research
Episode: The Wizard of Oz in Medical AI
Dr. Nigam Shah
Professor of Medicine and Chief Data Scientist at Stanford Health
Stanford Health / Stanford University
Episode: Teaching AI to Read Patient Histories
Emily Tat
Cardiology Fellow at Columbia University Medical Center
Columbia University Medical Center
Episode: Designing Trustworthy Clinical AI
Peter Brodeur
Cardiology Fellow at Harvard Medical School's Beth Israel Deaconess Medical Center
Harvard Medical School, Beth Israel Deaconess Medical Center
Episode: Designing Trustworthy Clinical AI
Sandro Galea
Editor-in-Chief of JAMA Health Forum; Inaugural dean of the School of Public Health at Washington University in St. Louis
Washington University in St. Louis
Episode: AI at the Policy Table
Viktor H. Ahlqvist
Researcher in the Unit of Integrative Epidemiology, Institute of Environmental Medicine at the Karolinska Institute
Karolinska Institute
Episode: AI Drug Safety in Pregnancy
Fang Yang
Professor in the Institute of Environmental Medicine, Karolinska Institutet
Karolinska Institutet
Episode: Understanding Disease Trajectories With AI
John Torous
Psychiatrist and associate professor of psychiatry, Beth Israel Deaconess Medical Center
Beth Israel Deaconess Medical Center / JAMA Psychiatry
Episode: AI Chatbots and Youth Mental Health
Robert Wachter
Professor and Chair, UCSF Department of Medicine
University of California, San Francisco
Episode: Leaping Forward Into… What? An Interview With Dr Robert Wachter

Hosts

Derek Angus
Host of Healthy Dialogue, senior editor at JAMA
Roy Perlis
Editor-in-Chief of JAMA Plus AI
Yulin Hswen
Associate Editor of JAMA and JAMA Plus AI; hosts various AI conversations in medicine

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Talking Points

Recent interactions between the hosts and their guests.

Teaching AI to Read Patient Histories
Q: What does the workflow look like when a clinician actually uses this system at the point of care?
There is a two-step setup: retrieve and load the patient timeline into a private model, then present a clean, integrated interface inside the EHR where the clinician can ask questions and receive concise, context-rich answers.
Teaching AI to Read Patient Histories
Q: Historically, large language models haven't been trained on patient data. How do you make them work with specific patients' records?
Models can be used as a processing engine on up-to-date patient data that is fed into a private, secure environment; the model handles general knowledge and literature, while the patient-specific records stay within a private EHR-enabled system to maintain privacy and accuracy.
Teaching AI to Read Patient Histories
Q: Can you talk to me about what a typical day looks like for a clinician piecing together a patient's history and diagnosing them using current systems?
Clinicians must comb through fragmented data across many specialties and documents, often manually assembling a patient timeline; the goal is to retrieve the relevant portions of that timeline quickly and accurately, which is where AI-enabled summarization and a unified interface could drastically cut the time from hours to minutes.
The Wizard of Oz in Medical AI
Q: Can you first tell me how did you evaluate what their intent was or what they were going to do next?
We categorized possible actions into four options (e.g., seek medical care today, monitor, etc.) and had dermatologists label the best next steps for each case to establish a ground truth against which participant choices were compared.
The Wizard of Oz in Medical AI
Q: Tell me about this really cool study that was in JAMA Dermatology, and you used the term Wizard of Oz setup.
The study used three arms—retrospective image review, standard Dermatology AI assistance, and a Wizard of Oz arm where the AI output was perfectly accurate by drawing on ground-truth diagnoses—to explore how AI influences laypeople's interpretation and decision-making about potential conditions and next steps.

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Frequently Asked Questions About JAMA+ AI Conversations

What is JAMA+ AI Conversations about and what kind of topics does it cover?

The show frames AI as a transformative force in medicine, hosting clinicians, researchers, and policy-focused voices to explore how artificial intelligence reshapes clinical practice, medical training, and health systems. Across episodes, conversations center on AI-assisted care, evaluation and safety of AI tools in real-world settings, data privacy and interoperability, and governance and regulatory considerations. A recurring strength is bringing together frontline clinicians with AI researchers to discuss practical design principles, evidence needs, and human–AI collaboration, often highlighting risks such as automation bias, disparities in usefulness, and the importance of preserving human judgment. The format tends to blend case studie... more

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Which podcasts are similar to JAMA+ AI Conversations?

These podcasts share a similar audience with JAMA+ AI Conversations:

1. JAMA Medical News
2. JAMA Editors' Summary
3. JAMA Clinical Reviews
4. NEJM AI Grand Rounds
5. Dwarkesh Podcast

How many episodes of JAMA+ AI Conversations are there?

JAMA+ AI Conversations launched 2 years ago and published 75 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on JAMA+ AI Conversations?

Recent guests on JAMA+ AI Conversations include:

1. Yun Liu
2. Dr. Nigam Shah
3. Emily Tat
4. Peter Brodeur
5. Sandro Galea
6. Viktor H. Ahlqvist
7. Fang Yang
8. John Torous

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