Discover the future of medicine with JAMA+ AI Conversations. This collection of interviews with clinicians, researchers, and AI experts explores how AI is impac...
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 University of Barcelona, discusses this and more with JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH. 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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AI Chatbots in Clinical Practice
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
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Discover the future of medicine with JAMA+ AI Conversations. This collection of interviews with clinicians, researchers, and AI experts explores how AI is impacting medicine – from clinical practice to training and research. Join us to uncover what lies ahead at the intersection of AI and medicine.
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