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<p>Automatic differential diagnosis (DDx) involves identifying potential conditions that could explain a patient’s symptoms and its accurate interpretation is of substantial significance. While large language models (LLMs) have demonstrated remarkable diagnostic accuracy, their capability to generate high-quality DDx explanations remains underexplored, largely due to the absence of specialized evaluation datasets and the inherent challenges of complex reasoning in LLMs. Therefore, building a tailored dataset and developing novel methods to elicit LLMs for generating precise DDx explanations are worth exploring. We developed the first publicly available DDx dataset, comprising expert-derived explanations for 570 clinical notes, to evaluate DDx explanations. Meanwhile, we proposed a novel framework, Dual-Inf, that could effectively harness LLMs to generate high-quality DDx explanations. To the best of our knowledge, it is the first study to tailor LLMs for DDx explanation and comprehensively evaluate their explainability. Overall, our study bridges a critical gap in DDx explanation, enhancing clinical decision-making.</p>

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Explainable differential diagnosis with dual-inference large language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月24日

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  • Shuang Zhou,
  • Mingquan Lin,
  • Sirui Ding,
  • Jiashuo Wang,
  • Canyu Chen,
  • Genevieve B. Melton,
  • James Zou,
  • Rui Zhang

摘要

Automatic differential diagnosis (DDx) involves identifying potential conditions that could explain a patient’s symptoms and its accurate interpretation is of substantial significance. While large language models (LLMs) have demonstrated remarkable diagnostic accuracy, their capability to generate high-quality DDx explanations remains underexplored, largely due to the absence of specialized evaluation datasets and the inherent challenges of complex reasoning in LLMs. Therefore, building a tailored dataset and developing novel methods to elicit LLMs for generating precise DDx explanations are worth exploring. We developed the first publicly available DDx dataset, comprising expert-derived explanations for 570 clinical notes, to evaluate DDx explanations. Meanwhile, we proposed a novel framework, Dual-Inf, that could effectively harness LLMs to generate high-quality DDx explanations. To the best of our knowledge, it is the first study to tailor LLMs for DDx explanation and comprehensively evaluate their explainability. Overall, our study bridges a critical gap in DDx explanation, enhancing clinical decision-making.