Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
arxiv(2024)
摘要
The development of large language models (LLMs) has brought unprecedented
possibilities for artificial intelligence (AI) based medical diagnosis.
However, the application perspective of LLMs in real diagnostic scenarios is
still unclear because they are not adept at collecting patient data
proactively. This study presents a LLM-based diagnostic system that enhances
planning capabilities by emulating doctors. Our system involves two external
planners to handle planning tasks. The first planner employs a reinforcement
learning approach to formulate disease screening questions and conduct initial
diagnoses. The second planner uses LLMs to parse medical guidelines and conduct
differential diagnoses. By utilizing real patient electronic medical record
data, we constructed simulated dialogues between virtual patients and doctors
and evaluated the diagnostic abilities of our system. We demonstrate that our
system significantly surpasses existing models, including GPT-4 Turbo, in both
disease screening and differential diagnoses. This research represents a step
towards more seamlessly integrating AI into clinical settings, potentially
enhancing the accuracy and accessibility of medical diagnostics.
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