@inproceedings{chen-etal-2025-cod,
    title = "{C}o{D}, Towards an Interpretable Medical Agent using Chain of Diagnosis",
    author = "Chen, Junying  and
      Gui, Chi  and
      Gao, Anningzhe  and
      Ji, Ke  and
      Wang, Xidong  and
      Wan, Xiang  and
      Wang, Benyou",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.740/",
    pages = "14345--14368",
    ISBN = "979-8-89176-256-5",
    abstract = "The field of AI healthcare has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these models remain largely unaddressed. This study introduces **Chain-of-Diagnosis (CoD)** to enhance the interpretability of medical automatic diagnosis. CoD transforms the diagnostic process into a diagnostic chain that mirrors a physician{'}s thought process, providing a transparent reasoning pathway. Additionally, CoD outputs the disease confidence distribution to ensure transparency in decision-making. This interpretability makes model diagnostics controllable and aids in identifying critical symptoms for inquiry through the entropy reduction of confidences. With CoD, we developed **DiagnosisGPT**, capable of diagnosing 9,604 diseases for validating CoD. Experimental results demonstrate that DiagnosisGPT outperforms other LLMs on automatic diagnostic tasks across three real-world benchmarks. Moreover, DiagnosisGPT provides interpretability while ensuring controllability in diagnostic rigor."
}