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arxiv:2407.13301

CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis

Published on Jul 18
· Submitted by jymcc on Jul 24
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Abstract

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.

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We propose the Chain-of-Diagnosis(CoD) to improve interpretability in medical diagnostics for LLMs. CoD features include:

  1. Transforming the opaque decision-making process into a five-step diagnostic chain that reflects a physician’s thought process.
  2. Producing a confidence distribution, where higher confidence suggests greater certainty in diagnosing a specific disease. CoD formalizes the diagnostic process as a process of reducing diagnostic certainty entropy.

Our code and data are available at: https://github.com/FreedomIntelligence/Chain-of-Diagnosis.

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Hi @jymcc , congrats! Thanks for publishing artifacts on the hub: https://huggingface.co/FreedomIntelligence.

It would be great to link the models and datasets to the paper, see here on how to do that: https://huggingface.co/docs/hub/en/datasets-cards#linking-a-paper

Interesting paper! Big kudos to your work!🔥

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