OpenEvidence (OE) is a retrieval-augmented clinical AI tool. We evaluated the references OE returned to 150 standardized prompts across five specialties, analyzing all 4979 citations. No reference was confirmed as fabricated; three abstracts had attribution errors. Most references were recent and high-impact, though jo...
Tami Lotan, Mahmud Omar, Y. Barash et al.· npj Health Systems· 0 citations
LLM clinical recommendations varied by sociodemographic descriptors, particularly under diagnostic uncertainty, suggesting structured inputs may mitigate bias in clinical AI systems.
Idit Tessler, Mahmud Omar, A. Wolfovitz et al.· Journal of Vestibular Resear...· 0 citations
Before AI-assisted triage enters rheumatology referral pathways, systems should separate objective clinical evidence from interpretive framing, and urgency and psychological attribution should be treated as auditable safety signals.
Mahmud Omar, M. Naffaa, R. Agbareia et al.· Rheumatology· 0 citations
Current evidence does not support the use of LLMs for diagnosis, treatment selection, or patient education in AD/MCI without clinician oversight, and are further limited by small heterogeneous evaluations, sparse hallucination measurement, and absence of prospective clinical validation.
Y. Adiniaev, Mahmud Omar, Oved Daniel et al.· Neurological Sciences· 0 citations
Under this benchmark, OpenEvidence produced fewer missed emergencies than the historical ChatGPT Health comparison, while errors concentrated in over-triage and evidence-seeking refusal.
Eric Jia, Mahmud Omar, Y. Barash et al.· International Journal of Med...· 0 citations
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