JAMA+ AI Conversations

• Can Open-Source LLMs Compete With Proprietary Ones for Complex Diagnoses?

  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?

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• Rethinking Race in Prenatal Screening for Open Neural Tube Defects

  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

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• AI’s Role in Advancing Equity for Individuals With Developmental Disabilities

  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

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• Prescreening for Clinical Trial Eligibility Using Large Language Models

  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

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• Machine Learning for Earlier Diagnosis of Schizophrenia

  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

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