CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
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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Junying Chen
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2024-07-24T08:22:44.371Z
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Junying Chen
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jymcc
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Chi Gui
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Anningzhe Gao
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2024-07-25T11:07:35.739Z
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Anningzhe
user
ganz1994
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Ke Ji
669f912ef04bf7220200e018
Xidong Wang
admin_assigned
2024-07-24T08:23:18.329Z
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Xidong Wang
user
Xidong
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Xiang Wan
669f912ef04bf7220200e01a
Benyou Wang
admin_assigned
2024-07-24T08:23:44.161Z
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Wang
user
Benyou
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
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.
Chain-of-Diagnosis (CoD) enhances interpretability in LLM-based medical diagnostics by providing a transparent reasoning pathway and developing DiagnosisGPT, which diagnoses a wide range of diseases with high accuracy and controllable rigor.
large language models (LLMs) Chain-of-Diagnosis (CoD) diagnostic chain physician's thought process disease confidence distribution DiagnosisGPT entropy reduction diagnostic benchmarks diagnostic rigor