ArticlePatterns (New York, N.Y.)2026
Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical question answering, but their reliability under model variability remains unclear. We evaluated 34 LLMs on 169 expert-curated, text-only, multiple-choice radiology questions, comparing zero-shot inference with a fixed shared-evidence condition in which all models received identical structured evidence reports before answer selection. Primary endpoints were inter-model decision dispersion and panel-based robustness of correctness. Shared evidence reduced decision dispersion (median entropy 0.48→0.13;
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