Abstract Objective To evaluate public perspectives on artificial intelligence (AI) in healthcare in the USA and assess variation in trust, comfort and perceived fairness across demographic groups. Methods and analysis This cross-sectional survey of US adults was conducted between 21 November and 4 December 2025, via Yo...
Vishnu Nair, R. Handler, M. Tandel et al.· BMJ digital health & AI· 0 citations
BACKGROUND
Stigmatizing language is common in the medical record and can perpetuate bias, which has downstream effects on clinical decision-making and patient care. Internal medicine faculty, residents, and advanced practice providers (APPs) are front-line clinicians for composing documentation, and faculty and APPs ar...
J. Caton, Bo Sun, Natasha Z. R. Steele et al.· Journal of general internal...· 0 citations
Abstract Clinical artificial intelligence (AI) has advanced rapidly, with frontier large language models now matching or exceeding physician performance on simulated diagnostic reasoning and clinical decision-support tasks. Yet adoption has outpaced the evidence base: fewer than 5% of cleared U.S. Food and Drug Adminis...
J. Worth, Anastasia Perez, David Wu et al.· BMJ digital health & AI· 1 citation
This work aims to give medical educators a practical guide for judging which uses of generative AI support skill formation at each stage of training and which displace it, and proposes a framework built on cognitive displacement, which locates each risk where it has the greatest potential for harm.
Nikhil S. Patel, Andre Kumar, Jeffrey Chi et al.· BMJ digital health & AI· 0 citations
The findings of this study suggest that an LLM-powered human-in-the-loop agentic workflow could accurately triage surgical patients for a surgical comanagement service.
Janelle B. Wang, T. Keyes, April S. Liang et al.· JAMA Network Open· 0 citations
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