JAMA+ AI Conversations

In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Rochelle Walensky, MD, MPH, professor of medicine at Harvard Medical School and former CDC Director, discuss the potential for AI deployment in health care to adversely impact women physicians, and why simply avoiding these technologies is not a realistic response.

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Will Women Physicians Face an AI Gap?
Women, Medicine, and AI

Yulin Hswen, ScD, MPH, Associate Editor of JAMA+ AI, speaks with Advanced Research Projects Agency for Health (ARPA-H) Program Manager Bon Ku, MD, MPP, about rethinking AI in health care from alarm fatigue and aviation-style simulation to patient-centered design and what it will take to safely move AI from promising models to real-world care.

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Beyond the AI Alarm


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Bon Ku's Bio

In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Eric Topol, MD, move from the science of aging to how to think about AI-enabled medicine. Topol discusses what it will take for AI to move medicine from reactive to predictive, why some well-validated imaging AI remains underused while large language models have spread rapidly, and whether AI is actually giving clinicians more time with patients.

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From Aging to AI and Beyond

In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Haider Warraich, MD, sort out the prospects for AI-driven heart failure care that doesn't entail a human in the loop. Warraich is a program manager at ARPA-H, the Advanced Research Projects Agency for Health, responsible for ADVOCATE, an effort to support the development of AI to guide heart failure treatment, seeking to create AI agents that can pass muster with the FDA.

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ADVOCATing for Patients With Heart Failure
FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine

In this episode of JAMA+ AI Conversations, Roy Perlis, MD, MSc, and Joseph Geraci, PhD, of Queen's University discuss using machine learning to analyze clinical trials and related FDA interactions. Geraci's work focuses on identifying subpopulations with different responses to help plan and analyze future trials.

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Enriching Clinical Trials With Machine Learning