DoPI: Doctor-like Proactive Interrogation LLM for Traditional Chinese Medicine
Zewen Sun, Ruoxiang Huang, Jiahe Feng, Rundong Kong, Yuqian Wang, Hengyu Liu, Ziqi Gong, Yuyuan Qin, Yingxue Wang, and Yu Wang

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…
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