CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis

The field of AI healthcare has undergone a significant transformation with the advent of large language models (LLMs), yet the challenge of interpretability in 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, Diagnosis-GPT provides interpretability while ensuring controllability in diagnostic rigor. Code, datasets, and models are publicly available at https://github.com/FreedomIntelligence/Chainof-Diagnosis .