@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://aclanthology.org/2025.findings-acl.740/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
%A Chen, Junying
%A Gui, Chi
%A Gao, Anningzhe
%A Ji, Ke
%A Wang, Xidong
%A Wan, Xiang
%A Wang, Benyou
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-cod
%X 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.
%R 10.18653/v1/2025.findings-acl.740
%U https://aclanthology.org/2025.findings-acl.740/
%U https://doi.org/10.18653/v1/2025.findings-acl.740
%P 14345-14368
Markdown (Informal)
[CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis](https://aclanthology.org/2025.findings-acl.740/) (Chen et al., Findings 2025)
ACL
- Junying Chen, Chi Gui, Anningzhe Gao, Ke Ji, Xidong Wang, Xiang Wan, and Benyou Wang. 2025. CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis. In Findings of the Association for Computational Linguistics: ACL 2025, pages 14345–14368, Vienna, Austria. Association for Computational Linguistics.