JAMA+ AI Conversations

• Patient Satisfaction With AI-Generated Responses

  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

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• Drafting Replies to Patient Messages With AI

  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?

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• Bioethics and AI

  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

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• AI-Based Suicide Screening for American Indian Patients

  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

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• Comparing Early Hospital Warning Scores for Clinical Deterioration

  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

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