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October 13, 2025Open Access

Evaluating Large Language Models for Evidence-Based Clinical Question Answering

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Authors

CWCan WangYCYiqun Chen

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Overview

Observational analysis reveals high accuracy for structured guidelines in clinical settings, indicating LLMs' potential.

Key Points

  • Accuracy is highest at 90% for structured guideline recommendations, but only 60-70% for narrative questions.
  • Each doubling of citation count correlates with a 30% increase in correct response odds from LLMs.
  • Retrieval-augmented prompting raises accuracy significantly, showcasing an effective strategy for enhancing LLMs.
  • The findings underscore both the promise and limitations of LLMs in evidence-based clinical question answering.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68ecfebf950606aabec09282https://doi.org/10.48550/arxiv.2509.10843
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses2025 · 12 citations
  2. 2Evaluation of large language models as a diagnostic tool for medical learners and clinicians using advanced prompting techniques2025 · 10 citations
  3. 3Clinical Assessment of Large Language Models: A Comprehensive Multi-domain Performance Study for Healthcare Applications2025 · 1 citations
  4. 4Benchmarking large language models on the United States medical licensing examination for clinical reasoning and medical licensing scenarios2025 · 23 citations
  5. 5Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making2025