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<p>Medical diagnosis is a complex, iterative process that relies heavily on clinicians’ reasoning and judgment. Traditional models, while able to provide consistent diagnostic results, fail to replicate the reasoning process of clinicians, making their outputs difficult to understand and justify. In this paper, we address this limitation by first generating clinical notes that capture the clinician’s diagnostic reasoning. These notes are then used to train a large language model, allowing it to mimic the step-by-step reasoning employed by clinicians during diagnosis. Our method introduces a hierarchical agent reflection mechanism to generate clinical notes, which deconstructs the diagnostic process into key stages, each handled by specialized agents. This structured approach not only improves the accuracy and reliability of the generated clinical notes but also ensures that the model’s reasoning aligns with human clinical practice. Experimental results show that models trained on this data outperform both general-purpose large language models and domain-specific medical models in diagnostic tasks. The proposed method enhances diagnostic transparency and interpretability, offering a valuable tool for AI-assisted clinical decision-making.</p>

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Hierarchical agent reflection for aligning LLM reasoning with clinical diagnostic processes

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Xinda Wang,
  • Xiaotong Li,
  • Deng Zhao,
  • Kehua Feng,
  • Lei Liang,
  • Zhiqiang Zhang,
  • Keyan Ding,
  • Huajun Chen,
  • Bo Wan,
  • Qiang Zhang

摘要

Medical diagnosis is a complex, iterative process that relies heavily on clinicians’ reasoning and judgment. Traditional models, while able to provide consistent diagnostic results, fail to replicate the reasoning process of clinicians, making their outputs difficult to understand and justify. In this paper, we address this limitation by first generating clinical notes that capture the clinician’s diagnostic reasoning. These notes are then used to train a large language model, allowing it to mimic the step-by-step reasoning employed by clinicians during diagnosis. Our method introduces a hierarchical agent reflection mechanism to generate clinical notes, which deconstructs the diagnostic process into key stages, each handled by specialized agents. This structured approach not only improves the accuracy and reliability of the generated clinical notes but also ensures that the model’s reasoning aligns with human clinical practice. Experimental results show that models trained on this data outperform both general-purpose large language models and domain-specific medical models in diagnostic tasks. The proposed method enhances diagnostic transparency and interpretability, offering a valuable tool for AI-assisted clinical decision-making.