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<p>Although Large Language Models (LLMs) possess extensive medical knowledge, they often struggle to emulate the complex, iterative process of real-world clinical diagnosis. To address this limitation, we present ClinDiag-GPT, a specialized LLM fine-tuned to execute full diagnostic procedures, supported by the ClinDiag-Framework evaluation system and ClinDiag-Benchmark, a dataset comprising 4,421 real-world cases. Our evaluation shows that existing LLMs, including GPT-4o-mini, GPT-4o, Claude-3-Haiku, Qwen2.5-72b, Qwen2.5-32b, and Qwen2.5-14b, while proficient in static tasks, fall short in dynamic diagnostic workflows and frequently commit clinical errors. In contrast, ClinDiag-GPT, trained on clinical cases, outperforms all baseline models in both diagnostic accuracy and procedural performance. Furthermore, a comparative analysis reveals that collaboration between physicians and ClinDiag-GPT yields higher diagnostic accuracy and efficiency compared to either working alone, demonstrating the utility of ClinDiag-GPT as a clinical assistant.</p>

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Grounding large language models in clinical diagnostics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Xi Chen,
  • Hanyu Zhou,
  • Huahui Yi,
  • Mingke You,
  • WeiZhi Liu,
  • Li Wang,
  • Ziyuan Qin,
  • Hairui Li,
  • Xue Zhang,
  • Yingman Guo,
  • Songlin Li,
  • Yunan Hu,
  • Qianjiang Xiong,
  • Rongxiao Li,
  • Lei Fan,
  • Qicheng Lao,
  • Weili Fu,
  • Jian Li,
  • Kang Li

摘要

Although Large Language Models (LLMs) possess extensive medical knowledge, they often struggle to emulate the complex, iterative process of real-world clinical diagnosis. To address this limitation, we present ClinDiag-GPT, a specialized LLM fine-tuned to execute full diagnostic procedures, supported by the ClinDiag-Framework evaluation system and ClinDiag-Benchmark, a dataset comprising 4,421 real-world cases. Our evaluation shows that existing LLMs, including GPT-4o-mini, GPT-4o, Claude-3-Haiku, Qwen2.5-72b, Qwen2.5-32b, and Qwen2.5-14b, while proficient in static tasks, fall short in dynamic diagnostic workflows and frequently commit clinical errors. In contrast, ClinDiag-GPT, trained on clinical cases, outperforms all baseline models in both diagnostic accuracy and procedural performance. Furthermore, a comparative analysis reveals that collaboration between physicians and ClinDiag-GPT yields higher diagnostic accuracy and efficiency compared to either working alone, demonstrating the utility of ClinDiag-GPT as a clinical assistant.