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Large Language Models Perform Diagnostic Reasoning

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arxiv 2307.08922 v1 pith:5SRZDBSG submitted 2023-07-18 cs.CL

Cheng-Kuang Wu , Wei-Lin Chen , Hsin-Hsi Chen This is my paper
classification cs.CL
keywords reasoninglanguagelargemodelspromptingdiagnosticdr-cotaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.

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Cited by 4 Pith papers

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    cs.CL 2026-08 conditional novelty 6.0 of 10

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  3. Medical Reasoning with Large Language Models: A Survey and MR-Bench

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    LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.

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