Lung ultrasound aids in the diagnosis of patients with dyspnea but requires technical proficiency for image acquisition. Cristiana Baloescu, MD, MPH, of Yale School of Medicine, joins JAMA Associate Editor Yulin Hswen, ScD, MPH, to discuss a new study published in JAMA Cardiology evaluating the ability of AI to guide acquisition of diagnostic-quality lung ultrasound images by trained health care professionals. Related Content:

• AI-Guided Lung Ultrasounds Could Help Nonexpert Clinicians Acquire “Expert-Level” Images
• Artificial Intelligence–Guided Lung Ultrasound by Nonexperts

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

How do patients feel about the quality of AI-generated responses to their messages to clinicians? Author Eleni Linos, MD, DrPH, of Stanford joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss her recent study in JAMA Network Open that characterized satisfaction with these messages. Related Content:

• Study Finds People Prefer AI Over Clinician Responses to Questions in the Electronic Medical Record
• Perspectives on Artificial Intelligence–Generated Responses to Patient Messages

Weight-Loss Drugs Are Reshaping the Future of Bariatric Surgery; Are Compounded GLP-1 Drugs Safe? Will Renaming Some Cancers as Noncancers Hurt or Help? Related Content:

• What Does the Rise of GLP-1 Drugs Mean for Bariatric Surgery?
• Patients Are Flocking to Compounded Weight-Loss Drugs, but Are They Safe?
• Experts Are Debating Whether Some Cancers Shouldn’t Be Called That

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