Mahmud Omar

The missing links in agentic AI autonomy

Reference quality of OpenEvidence across five medical specialties

This study evaluated the references OE returned to 150 standardized prompts across five specialties, analyzing all 4979 citations and found most references were recent and high-impact, though journal concentration varied by specialty.

Small language models in clinical medicine: a systematic review of performance, safety, and deployment feasibility.

Routine reporting of calibration and uncertainty and measurement of end-to-end clinical performance are prerequisites for responsible SLM deployment.

Sociodemographic bias in LLMs' clinical decision-making for dizziness.

LLM clinical recommendations varied by sociodemographic descriptors, particularly under diagnostic uncertainty, suggesting structured inputs may mitigate bias in clinical AI systems.

AI-assisted rheumatology triage changes with referral framing.

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.

Generative large language models in the clinical management of Alzheimer’s disease and mild cognitive impairment

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.

OpenEvidence errs on the safe side in a structured test of triage recommendations.

Under this benchmark, OpenEvidence produced fewer missed emergencies than the historical ChatGPT Health comparison, while errors concentrated in over-triage and evidence-seeking refusal.