DoPI: Doctor-like Proactive Interrogation LLM for Traditional Chinese Medicine

TL;DR

The paper introduces DoPI, a specialized LLM system for Traditional Chinese Medicine that improves multi-turn dialogue and diagnosis accuracy through a collaborative architecture and a new evaluation method.

Contribution

It presents a novel collaborative LLM architecture for TCM diagnosis, including a guidance and expert model, and develops a new dataset and evaluation methodology for realistic simulations.

Findings

Achieves 84.68% accuracy in diagnosis tasks.

Enhances multi-turn dialogue capabilities in TCM diagnosis.

Introduces a new dataset and evaluation method for TCM AI systems.

Abstract

Enhancing interrogation capabilities in Traditional Chinese Medicine (TCM) diagnosis through multi-turn dialogues and knowledge graphs presents a significant challenge for modern AI systems. Current large language models (LLMs), despite their advancements, exhibit notable limitations in medical applications, particularly in conducting effective multi-turn dialogues and proactive questioning. These shortcomings hinder their practical application and effectiveness in simulating real-world diagnostic scenarios. To address these limitations, we propose DoPI, a novel LLM system specifically designed for the TCM domain. The DoPI system introduces a collaborative architecture comprising a guidance model and an expert model. The guidance model conducts multi-turn dialogues with patients and dynamically generates questions based on a knowledge graph to efficiently extract critical symptom information. Simultaneously, the expert model leverages deep TCM expertise to provide final diagnoses and treatment plans. Furthermore, this study constructs a multi-turn doctor-patient dialogue dataset to simulate realistic consultation scenarios and proposes a novel evaluation methodology that does not rely on manually collected real-world consultation data. Experimental results show that the DoPI system achieves an accuracy rate of 84.68 percent in interrogation outcomes, significantly enhancing the model's communication ability during diagnosis while maintaining professional expertise.