JAMA+ AI Conversations

Daniel Lindsay, an economist from QIMR in Queensland, dives into the innovative world of 3D total-body photography for melanoma detection. He discusses the recent study on the cost-effectiveness of this technology compared to traditional methods. The conversation touches on the role of AI in enhancing lesion detection while navigating the challenges of its economic evaluation. Lindsay also highlights unexpected findings related to excisions and the implications for clinical practice, emphasizing the need for reevaluating healthcare strategies.

Robert S. Rudin, a Senior Information Scientist at RAND and a Professor of Policy Analysis, discusses a revolutionary AI app for asthma symptom tracking. He highlights how the app aims to enhance patient adherence and streamline communication with healthcare providers. The conversation dives into the collaborative design process behind the app and its user-centered focus. Rudin also addresses accessibility challenges and envisions how this technology could evolve to monitor other chronic conditions, transforming digital health management.

The Dana-Farber Cancer Institute (DFCI)’s MatchMiner tool was developed to increase historically low clinical trial enrollment rates in adults with cancer. Roy Perlis, MD, MSc, Editor in Chief of JAMA+ AI, spoke with Kenneth Kehl, MD, MPH, about his recent study published in JAMA Network Open evaluating the AI tool’s ability to fulfill its purpose through genome sequencing. Related Content: Clinical Trial Notifications Triggered by Artificial Intelligence–Detected Cancer Progression Considerations in Translating AI to Improve Care How AI Could Increase Clinical Trial Enrollment in Adults With Cancer

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