ArticleFrontiers in medicine2026
Reasoning vs. conventional large language models for BI-RADS educational questions answering: a multi-model comparative evaluation.
Article in Frontiers in medicine, 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
Objective: To compare reasoning vs. conventional large language models (LLMs) in generating answers with guideline-aligned explanations for Breast Imaging Reporting and Data System (BI-RADS) educational questions. Methods: In this prospective study performed from February 6 to 12, 2025, 49 English-Chinese question pairs were extracted from BI-RADS Atlas Fifth Edition. Two reasoning LLMs (ChatGPT-o1, Deepseek-R1) and six conventional LLMs (Gemini2.0-Flash, Deepseek-V3, ChatGPT-4o, ChatGPT-3.5, Qwen-2.5, and WenXinYiYan-3.5) generated answers and explanations to the questions through structured prompts. Three radiologists specialized in breast imaging independently evaluated responses using a 5-point Likert scale, with reference to standard answers. Results: The reasoning LLMs significantly outperformed conventional models (median [interquartile range (IQR)]: 3.7 [2.7-4.0] vs. 2.7 [2.0-3.7], Conclusions: Reasoning LLMs show significant potential for BI-RADS guideline explanation and education, but require specific optimization for complex clinical scenario instruction.
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