DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis

2026-03-30

AI Summary

This study addresses three key challenges in integrative Chinese and Western medicine for gastrointestinal disorders: the scarcity of high-quality data, the disconnect between traditional Chinese medicine (TCM) pattern differentiation and Western diagnostic logic, and the absence of standardized evaluation benchmarks. To tackle these issues, the authors propose the DongYuan framework, which introduces SSDF-Bench—a dedicated high-quality dataset and evaluation benchmark—and SSDF-Core, a large language model trained via a two-stage paradigm that integrates TCM syndrome differentiation with Western diagnostic reasoning through supervised fine-tuning (SFT) and direct preference optimization (DPO). Additionally, the framework incorporates SSDF-Navigator, a plug-and-play inquiry navigation module to refine clinical questioning strategies. Experimental results demonstrate that SSDF-Core significantly outperforms twelve mainstream baseline models on SSDF-Bench, establishing a methodological and technical foundation for intelligent integrative diagnosis.

Abstract

The clinical burden of spleen-stomach disorders is substantial. While large language models (LLMs) offer new potential for medical applications, they face three major challenges in the context of integrative Chinese and Western medicine (ICWM): a lack of high-quality data, the absence of models capable of effectively integrating the reasoning logic of traditional Chinese medicine (TCM) syndrome differentiation with that of Western medical (WM) disease diagnosis, and the shortage of a standardized evaluation benchmark. To address these interrelated challenges, we propose DongYuan, an ICWM spleen-stomach diagnostic framework. Specifically, three ICWM datasets (SSDF-Syndrome, SSDF-Dialogue, and SSDF-PD) were curated to fill the gap in high-quality data for spleen-stomach disorders. We then developed SSDF-Core, a core diagnostic LLM that acquires robust ICWM reasoning capabilities through a two-stage training regimen of supervised fine-tuning. tuning (SFT) and direct preference optimization (DPO), and complemented it with SSDF-Navigator, a pluggable consultation navigation model designed to optimize clinical inquiry strategies. Additionally, we established SSDF-Bench, a comprehensive evaluation benchmark focused on ICWM diagnosis of spleen-stomach disorders. Experimental results demonstrate that SSDF-Core significantly outperforms 12 mainstream baselines on SSDF-Bench. DongYuan lays a solid methodological foundation and provides practical technical references for the future development of intelligent ICWM diagnostic systems.

Problem

• Integrative Chinese and Western Medicine
• Spleen-Stomach Disorders
• Large Language Models
• Diagnostic Reasoning
• Evaluation Benchmark

Innovation

• Integrative Chinese and Western Medicine
• Large Language Models
• Spleen-Stomach Disorders
• Diagnostic Reasoning
• Evaluation Benchmark