GutGPT: A multidimensional knowledge-enhanced large language model for gastrointestinal medicine

Abstract

Background

Gastrointestinal (GI) diseases are common, chronic conditions that require personalized, long-term management, placing a heavy burden on traditional healthcare systems. While large language models (LLMs) offer potential for supporting patient care with personalized and empathetic guidance, existing models often lack domain-specific knowledge in GI diseases and suffer from issues like slow convergence and overfitting.

Methodology

We first construct a high-quality GI disease QA dataset comprising 191,615 entries from diverse sources: real-world doctor-patient dialogues, medical knowledge graphs, medical guidelines, and Chinese medical licensing exam data. Then, we introduce GutGPT, an LLM fine-tuned from Baichuan-13B-Chat using Low-Rank Adaptation (LoRA) technology with self-attention mechanism parameter sharing. To evaluate the performance of GutGPT and other existing LLMs, we use a combination of expert evaluation and public dataset testing to comprehensively assess each model’s accuracy and empathy.

Results

We conduct comprehensive evaluations, including expert evaluations and evaluations on multiple benchmark datasets. The results show that our model outperforms 16 existing methods and achieves state-of-the-art performance. In expert evaluations, GutGPT improves diagnostic accuracy by 9.59% compared to the baselines. On two public medical QA datasets, CMB and CMExam, it achieves an average accuracy improvement of 22.47%.

Conclusions

GutGPT achieves high accuracy in managing GI disease patients and demonstrates strong empathy. It serves as an important auxiliary tool for both patients and physicians in disease management.