Sep 2026· JAMIA Journal of the American Medical Informatics Association· Vol 33, pp. 2177-2185· 0 citations
Medicine
TL;DR
Pairing clinician Likert assessments with transcript-level error audits may be a useful adjunct in evaluating ADS and other generative AI clinical tools; confirmation in larger studies is needed before this framework drives procurement decisions.
Objective To compare expert-rated quality and safety of responses from four contemporary large language models (LLMs)—ChatGPT, Gemini, Claude, and Grok—on expert-level anterior segment ophthalmology questions. Design Cross-sectional, masked, randomized-display-order, multievaluator comparative study. Participants Eleve...
Soonwon Yang, H. Han, H. Lee· Ophthalmology Science· 0 citations
Audited three commercial AI scribes on the same 142 consultations: 565 notes from recorded UK primary-care and US ambulatory encounters plus authored scenarios, with one failure mode drawn from published scribe-error taxonomies.
Sebastian Fox, L. Markham, Ryan Lail et al.· 1 citation· ⚡1
Background: Automated assessment of relational competencies by large language models (LLMs) is gaining traction in medical education, yet its empirical reliability remains poorly documented. Methods: We evaluated ATENA, a GPT-3.5-Turbo virtual-patient simulator deployed at the Faculty of Medicine of Nice (France), in t...
C. Drouot, Alain Percivalle· Education sciences· 0 citations
Consumer-facing artificial intelligence platforms are increasingly used to obtain health information, but the quality of their responses to dental radiology questions requires evaluation. This study compared expert ratings of single responses generated by three selected consumer-facing AI platform configurations to...
Deepika Kapoor, D. Garg, A. Nirola et al.· Frontiers in Oral Health· 0 citations
Assess effects of an ambient artificial intelligence (AI) scribe on documentation time, note quality, clinician and patient experience in clinician–patient encounters.
Prospective, multisite, mixed-methods, before-and-after cohort study of an ambient voice to text AI scribe generating clinical notes conducte...
Ian A. Scott, K. Stockton, Manaan Kar Ray et al.· BMJ Connections Digital Heal...· 0 citations
Clinical documentation is essential to healthcare delivery but is timeconsuming, cognitively demanding, and prone to error. Large language models (LLMs) can generate draft notes, but may introduce hallucinations, unsupported inferences, or factual distortions that clinicians overlook. This study evaluated whether trans...
Abdus Samad· Proceedings of the Human Fac...· 0 citations
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