JAMA+ AI Conversations

Delaying diagnosis of parkinsonism can mean delaying care. In a study recently published in JAMA Neurology, David Vaillancourt, PhD, and colleagues tested the ability of an AI model to differentiate between Parkinson disease and other neurodegenerative disorders when paired with MRI. He joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH to discuss. Related Content: A Large Proportion of Parkinson Disease Diagnoses Are Wrong—Here’s How AI Could Help Automated Imaging Differentiation for Parkinsonism

Adam Chekroud, founder of Spring Health, and Molly Candon, an economist at the University of Pennsylvania, discuss the vital role of employer-sponsored digital mental health programs. They explore the financial return on investment of these initiatives, emphasizing the increased productivity and well-being they can offer. The conversation highlights the evolution of mental health support post-pandemic and the need for integrated platforms that improve user experience and treatment effectiveness. AI's transformative potential in mental health care delivery also takes center stage.

Susan Athey, PhD, of Standford University joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss her research on machine learning to target behavioral nudges for college students and their potential implications for health care. Related Content: How an Economist’s Application of Machine Learning to Target Nudges Applies to Precision Medicine

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.

Arjun K. Manrai, from Harvard Medical School, discusses the intriguing findings of a recent study comparing open-source and proprietary large language models for complex medical diagnoses. He highlights how institutions can utilize custom open-source models while maintaining data privacy. The conversation dives into the competitiveness of models like LLAMA 3.1 against GPT-4, the implications for healthcare technology investment, and the critical role of AI in clinical practice, emphasizing the necessity of human oversight in ensuring diagnostic reliability.

Daniel Herman, MD, PhD, from the University of Pennsylvania, dives into the complexities of prenatal screening for open neural tube defects. He discusses the historical implications of race in these screenings, revealing how racial adjustments can lead to misleading false positives, particularly for Black patients. The conversation highlights a significant shift towards race-agnostic approaches, advocating for more equitable prenatal testing methods. Herman emphasizes the necessity for ongoing research to explore new biomarkers that can improve healthcare outcomes for all.

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

Join Søren Dinesen Østergaard, a professor at Aarhus University, as he dives into the groundbreaking use of machine learning in predicting the onset of schizophrenia and bipolar disorder. He discusses the significant hurdles in early diagnosis and how timely interventions can improve outcomes. Østergaard also highlights the challenges of varying predictive model performance across hospitals and the need for dynamic, individualized approaches that integrate clinical data for better accuracy. It's a fascinating look at the future of mental health diagnosis!

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