BACKGROUND
Artificial intelligence (AI)-based systems show promise for assisting primary care providers (PCPs) with patient care. We aimed to evaluate the safety and quality of clinical conversations of a patient-facing conversational AI system, which engaged in real-world urgent primary care appointments.
METHODS
In...
P. Brodeur, Jacob M. Koshy, Anil Palepu et al.· The Lancet· 1 citation
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...
John Emmett Worth, Anastasia Perez, David Wu et al.· BMJ digital health & AI· 1 citation
Advances to the artificial intelligence system AMIE enable it to manage disease across multiple visits while reasoning over a corpus of clinical guidelines and patient history, outperforming primary care physicians on management reasoning tasks.
Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). This is difficult to scale; thus, prior studies typically rely on small physician panels, often from a single institution or specialty, which both limits the scientific ques...
Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur et al.· 0 citations
Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation illusion: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. We introduce...
Abin Roy, Afthab Salam Kanniyan, Jawadh Abdul Kabeer et al.· arXiv.org· 0 citations
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