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<p>Large Language Models (LLMs) have demonstrated considerable potential in general practice. However, existing benchmarks and evaluation frameworks primarily depend on exam-style or simplified question-answer formats, lacking a competency-based structure aligned with the real-world clinical responsibilities encountered in general practice. Consequently, the extent to which LLMs can reliably fulfill the duties of general practitioners (GPs) remains uncertain. In this work, we propose a novel evaluation framework to assess the capability of LLMs to function as GPs. Based on this framework, we introduce a general practice benchmark (GPBench), whose data are meticulously annotated by domain experts in accordance with routine clinical practice standards. We evaluate ten state-of-the-art LLMs and analyze their competencies. Our findings indicate that current LLMs are not suitable for autonomous deployment in clinical general practice and that all realistic applications require continuous human oversight; further optimization specifically tailored to the daily responsibilities of GPs remains essential.</p>

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Evaluating clinical competencies of large language models with a general practice benchmark

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Zheqing Li,
  • Yiying Yang,
  • Jiping Lang,
  • Wenhao Jiang,
  • Junrong Chen,
  • Yuhang Zhao,
  • Shuang Li,
  • Dingqian Wang,
  • Zhu Lin,
  • Xuanna Li,
  • Yuze Tang,
  • Jiexian Qiu,
  • Xiaolin Lu,
  • Hongji Yu,
  • Shuang Chen,
  • Yuhua Bi,
  • Xiaofei Zeng,
  • Yixian Chen,
  • Lin Yao

摘要

Large Language Models (LLMs) have demonstrated considerable potential in general practice. However, existing benchmarks and evaluation frameworks primarily depend on exam-style or simplified question-answer formats, lacking a competency-based structure aligned with the real-world clinical responsibilities encountered in general practice. Consequently, the extent to which LLMs can reliably fulfill the duties of general practitioners (GPs) remains uncertain. In this work, we propose a novel evaluation framework to assess the capability of LLMs to function as GPs. Based on this framework, we introduce a general practice benchmark (GPBench), whose data are meticulously annotated by domain experts in accordance with routine clinical practice standards. We evaluate ten state-of-the-art LLMs and analyze their competencies. Our findings indicate that current LLMs are not suitable for autonomous deployment in clinical general practice and that all realistic applications require continuous human oversight; further optimization specifically tailored to the daily responsibilities of GPs remains essential.