Artificial intelligence (AI) in health care is advancing, despite concerns about how its use may impact health disparities. Dimitri Christakis, MD, MPH, chief health officer at Special Olympics, joins JAMA Associate Editor Yulin Hswen, ScD, MPH, to discuss AI's potential role in improving health care delivery for people with intellectual and developmental disabilities. Related Content:

• How AI Could Improve Health Care for People With Intellectual and Developmental Disabilities
• How Artificial Intelligence Can Promote Inclusive Health

A recent study showed AI-assisted screening using a large language model tool reduced time to determine trial eligibility compared with manual methods. Author Alexander J. Blood, MD, MSc, cardiologist at Brigham and Women's Hospital, and Associate Director of the Accelerator for Clinical Transformation Research Group at Harvard Medical School joins JAMA Associate Editor Yulin Hswen, ScD, MPH, to discuss this topic and more. Related Content:

• Study Finds AI Can Quickly Prescreen Patients for Clinical Trials, Speeding Enrollment
• Manual vs AI-Assisted Prescreening for Trial Eligibility Using Large Language Models—A Randomized Clinical Trial

In a recent study published in JAMA Psychiatry, researchers reported that a machine learning model was able to stratify risk for subsequent diagnosis of schizophrenia or bipolar disorder among individuals already receiving psychiatric treatment. Coauthor Søren Dinesen Østergaard, PhD, of Aarhus University in Denmark joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss. Related Content:

• Machine Learning Model Shows Promise in Early Detection of Serious Mental Illness
• Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning

AI can play a role in addressing language barriers in health care. In a recent Editorial in JAMA Network Open, Pilar Ortega, MD, MGM, of the University of Illinois College of Medicine, and coauthors emphasized the urgent need for integrating language equity into digital health solutions. Dr Ortega joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss. Related Content:

• Researcher Proposes New Framework for Language Equity in Health Technology
• Language Equity in Health Technology for Patients With Non–English Language Preference
• Challenges to Video Visits for Patients With Non–English Language Preference

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

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