JAMA+ AI Conversations

Arthur Brant, Chief Resident in Ophthalmology at Stanford, and Sunny Virmani, Group Product Manager at Google, dive into the potential of AI in diabetic retinopathy screening. They discuss a groundbreaking study comparing AI's effectiveness to human evaluations, highlighting both opportunities and disparities in screening access. The conversation addresses challenges such as model drift and accountability in AI use. They stress the importance of ongoing monitoring and innovative solutions to improve patient care, especially in underserved areas.

A recent study published in JAMA Health Forum suggests that institutions may be able to deploy custom open-source large language models (LLMs) that run locally without sacrificing data privacy or flexibility. Coauthors Thomas A. Buckley, BS, and Arjun K. Manrai, PhD, from the Department of Biomedical Informatics at Harvard Medical School join JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss. Related Content: Can Open-Source AI Models Diagnose Complex Cases as Well as GPT-4?

Correction: This podcast has been updated to add additional context on the frequency of false positives. Open neural tube defects affect approximately 1 in 1400 births. Daniel Herman, MD, PhD, of the University of Pennsylvania Perelman School of Medicine joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss a quality improvement study examining the need to continue to incorporate race in tests that screen for these defects. Related Content: Study Findings Question Value of Including Race in Prenatal Screening for Birth Defects Reassessing the Inclusion of Race in Prenatal Screening for Open Neural Tube Defects

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

Cristiana Baloescu, an Assistant Professor of Emergency Medicine at Yale, specializes in using machine learning to improve ultrasound techniques. In this discussion, she unveils how AI can assist non-experts in obtaining diagnostic-quality lung ultrasound images. The conversation dives into AI's role in diagnosing respiratory issues like heart failure and COPD, enhancing timely treatment. Baloescu also outlines the hurdles of integrating AI in clinical settings, emphasizing its potential for improving care in diverse healthcare environments.

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