title: CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
summary: 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.
paper_authors._id: 669f912ef04bf7220200e014
paper_authors.name: Junying Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:22:44.371Z
paper_authors._id: 64097dd1b6a334f53e2b3e4c
paper_authors.avatarUrl: /avatars/18d036aab5e096054a8706bc78027126.svg
paper_authors.fullname: Junying Chen
paper_authors.type: user
paper_authors.user: jymcc
paper_authors._id: 669f912ef04bf7220200e015
paper_authors.name: Chi Gui
paper_authors._id: 669f912ef04bf7220200e016
paper_authors.name: Anningzhe Gao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-25T11:07:35.739Z
paper_authors._id: 66a2271fc56fba8f76bc12ed
paper_authors.avatarUrl: /avatars/ef0e9b600d5dac7f160a86fcc08e0b7d.svg
paper_authors.fullname: Anningzhe
paper_authors.type: user
paper_authors.user: ganz1994
paper_authors._id: 669f912ef04bf7220200e017
paper_authors.name: Ke Ji
paper_authors._id: 669f912ef04bf7220200e018
paper_authors.name: Xidong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:23:18.329Z
paper_authors._id: 640ed3e9f2d7c41a1e9a9fde
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1678693486092-640ed3e9f2d7c41a1e9a9fde.jpeg
paper_authors.fullname: Xidong Wang
paper_authors.type: user
paper_authors.user: Xidong
paper_authors._id: 669f912ef04bf7220200e019
paper_authors.name: Xiang Wan
paper_authors._id: 669f912ef04bf7220200e01a
paper_authors.name: Benyou Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:23:44.161Z
paper_authors._id: 637c6703ca8542a0ba900ccb
paper_authors.avatarUrl: /avatars/288ed63a1efa566c3f01e850c6ba5dd5.svg
paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: Benyou
paper_title: CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
paper_summary: 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.
paper_ai_keywords: Chain-of-Diagnosis (CoD) large language models (LLMs) Diagnostic chain Physician's thought process Disease confidence distribution Entropy reduction DiagnosisGPT