General-purpose large language models outperform specialized clinical AI tools on medical benchmarks
Krithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; Ali Hage; Sean N. Neifert; Cordelia Orillac; Nataniel J. Mandelberg; Hammad A. Khan · 2026 · Nature Medicine
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Specialized clinical artificial intelligence (AI) tools are entering medical practice despite scarce independent evaluation. We quantitatively evaluate two clinical AI tools, OpenEvidence and UpToDate Expert AI, built on large language models (LLMs) against three frontier LLMs: GPT-5.2, Gemini 3.1 Pro and Claude Opus 4.6. Our evaluation has three stages: (1) 500 MedQA questions testing medical knowledge, (2) 500 HealthBench items measuring alignment with clinicians and (3) the real clinical queries (RCQ) benchmark, built from 100 de-identified queries from physicians to a general-purpose langu
Abstract by Krithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; Ali Hage; Sean N. Neifert; Cordelia Orillac; Nataniel J. Mandelberg; Hammad A. Khan, Nature Medicine (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41591-026-04431-5