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In our latest JAMA+ AI Conversations episode, Cecilia Lee, MD, MS, of Washington University, St. Louis, takes us inside the rapidly evolving field of oculomics, where high‑resolution retinal imaging, deep learning, and multimodal datasets are beginning to reveal early signals of disease.
Dr Lee discusses the data challenges, need for strong validation, and ethical considerations slowing implementation, while also highlighting a future where a simple retinal scan, or even a smartphone image, could become a routine indicator of systemic health.
Learn more and listen to the podcast.
Editor’s Picks in this week’s JAMA+ AI:
- A study of the Operating Room Black Box, a novel intervention that leverages AI to improve surgical outcomes, identified four major gaps impairing implementation. Surgeons’ expectations of AI often exceeded what the system could deliver, underscoring the importance of aligning claims, workflows, and institutional support for successful surgical AI adoption.
- An Invited Commentary warns that surgical AI tools like OR Black Box risk entering a “trough of disillusionment” as clinicians confront unmet expectations, need for extensive local algorithmic training, and latency in the delivery of insights.
- In a randomized clinical trial of 524 adults with congenital heart disease, a tailored 4‑week digital emotion‑regulation program significantly improved emotion regulation, and improved well‑being, life satisfaction, anxiety, depression, and stress compared with usual care.
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