Cristiana Baloescu, an Assistant Professor of Emergency Medicine at Yale, specializes in using machine learning to improve ultrasound techniques. In this discussion, she unveils how AI can assist non-experts in obtaining diagnostic-quality lung ultrasound images. The conversation dives into AI's role in diagnosing respiratory issues like heart failure and COPD, enhancing timely treatment. Baloescu also outlines the hurdles of integrating AI in clinical settings, emphasizing its potential for improving care in diverse healthcare environments.

A recent study in JAMA Network Open evaluates the use of machine learning algorithms to assess the management of urinary tract infection (UTI). Author Sanjat Kanjilal, MD, MPH, professor in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Healthcare Institute, joins JAMA Associate Editor Yulin Hswen, ScD, MPH, to discuss this topic and more. Related Content: Researchers Use Machine Learning to Put Older Clinical Guidelines to the Test Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection

Digital health technologies, including patient portals, are widely used by older adults, as described in a recent study published in JAMA Network Open. Author Cornelius James, MD, of the University of Michigan joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss the study and how it fits with his own experience in the clinic. Related Content: Study Finds Most Older Adults Use Digital Health Technologies, Plus Some Surprises Use of Digital Health Technologies by Older US Adults

Eleni Linos, a prominent dermatologist and epidemiologist at Stanford, shares insights from her recent research on patient satisfaction with AI-generated responses to clinician messages. The conversation reveals that patients often prefer AI for its efficiency over human replies, highlighting a potential shift in healthcare communication. They also discuss how AI can help reduce clinician burnout while balancing patient expectations. Eleni emphasizes the importance of integrating AI responsibly to ensure transparency and compliance in medical interactions.

The burden of responding to clinician inbox messages may be a contributor to burnout. Eden English, MD, of UCHealth joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss her recent study published in JAMA Network Open, which examined the use of large language models to reply to patient messages. Related Content: Researchers Tested an AI Tool That Drafts Responses to Patient Messages—Here’s What They Found Utility of Artificial Intelligence–Generative Draft Replies to Patient Messages Are Artificial Intelligence–Generated Replies the Answer to the Electronic Health Record Inbox Problem?

With accelerating global adoption of AI, countries are developing ethical AI frameworks to prevent harm to the most vulnerable populations. Maria Villalobos-Quesada, PhD, from the National eHealth Living Lab (NeLL) in the Netherlands and the Observatory of Bioethics and Law of the University of Barcelona, discusses this and more with JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH.  *Author image and affiliations updated February 4, 2025. Related Content: Study Finds Limited Evidence to Support More Than 40 Predictive Machine Learning Algorithms Used in Primary Care Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care The Need for Continuous Evaluation of Artificial Intelligence Prediction Algorithms

American Indian and Alaska Native communities have higher rates of suicide than any other racial or ethnic group in the US. A recent study published in JAMA Network Open describes an AI-based suicide screening tool investigated in an American Indian community. Author Emily Haroz, PhD, of Johns Hopkins Bloomberg School of Public Health, joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH. Related Content: How AI Could Help Clinicians Identify American Indian Patients at Risk for Suicide Performance of Machine Learning Suicide Risk Models in an American Indian Population

How can hospitals use early warning score tools to risk stratify patients without adding to alarm fatigue? Dana Edelson, MD, MS, of the University of Chicago joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss a recent study published in JAMA Network Open that she coauthored, comparing 6 early warning scores designed to recognize clinical deterioration in hospitalized patients. Related Content: Researchers Compared Hospital Early Warning Scores for Clinical Deterioration—Here’s What They Learned Early Warning Scores With and Without Artificial Intelligence

Chatbots may have a role in enhancing clinical care, but the best way to apply them remains a work in progress. Jonathen Chen, MD, PhD, and Ethan Goh, MD, MS, of Stanford, join JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss their randomized clinical trial published in JAMA Network Open investigating the use of chatbots in clinical practice. Related Content: An AI Chatbot Outperformed Physicians and Physicians Plus AI in a Trial—What Does That Mean? Large Language Model Influence on Diagnostic Reasoning

In a recent Viewpoint published in JAMA, Michael Pencina, PhD, of Duke University, argued for a federated registration system for AI tools deployed in health systems. He joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss his article, the Coalition for Health AI (CHAI), and more. Related Content: Health Systems Are Struggling to Keep Up With AI—A National Registration System Could Help A Federated Registration System for Artificial Intelligence in Health