  The field of medical diagnosis 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
LLM-based medical diagnostics. 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 9604 diseases. Experimental
results demonstrate that DiagnosisGPT outperforms other LLMs on diagnostic
benchmarks. Moreover, DiagnosisGPT provides interpretability while ensuring
controllability in diagnostic rigor.
