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.

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• 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?

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• 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.

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• 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 University of Barcelona, discusses this and more with JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH.

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• 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.

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• 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.

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• 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.

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• An AI Chatbot Outperformed Physicians and Physicians Plus AI in a Trial—What Does That Mean?
• Large Language Model Influence on Diagnostic Reasoning

Why COVID-19 Vaccines Don’t Stop Transmission; Hospital-at-Home—But Make It Virtual

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• In Search of COVID-19 Vaccines That Elicit Mucosal Immunity and Stop Transmission
• The Next Step in Hospital-at-Home Care Could Be Virtual