Observational Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 21, 2025; 31(31): 109948
Published online Aug 21, 2025. doi: 10.3748/wjg.v31.i31.109948
DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and treatment
Xiao-Han Wan, Mei-Xia Liu, Yan Zhang, Guan-Jun Kou, Lei-Qi Xu, Han Liu, Xiao-Yun Yang, Xiu-Li Zuo, Yan-Qing Li
Xiao-Han Wan, Mei-Xia Liu, Yan Zhang, Guan-Jun Kou, Lei-Qi Xu, Han Liu, Xiao-Yun Yang, Xiu-Li Zuo, Yan-Qing Li, Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, China
Author contributions: Wan XH, Yang XY, Zuo XL, and Li YQ participated in conceptualization; Wan XH, Liu MX, Zhang Y, Yang XY, and Zuo XL participated in the methodology; Wan XH participated in the data curation, formal analysis, investigation, and writing of the original draft; Liu MX and Zhang Y participated in the model training; Zhang Y, Kou GJ, Xu LQ, Liu H, and Zuo XL made the evaluation scores; Li YQ participated in the supervision, writing of the manuscript, and editing. All authors contributed to the article and approved the submitted version.
Supported by China Health Promotion Foundation Young Doctors’ Research Foundation for Inflammatory Bowel Disease; Taishan Scholars Program of Shandong Province, China, NO. tsqn202306343; and National Natural Science Foundation of China, No. 82270580, No. 82070552, No. 82270578, and No. 82300599.
Institutional review board statement: This study did not involve human or animal experiments. Therefore, ethics committee approval was not required.
Informed consent statement: This study did not involve human or animal experiments. Therefore, informed consent was not required.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: Dataset available from the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yan-Qing Li, PhD, Professor, Department of Gastroenterology, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan 250012, Shandong Province, China. liyanqing@sdu.edu.cn
Received: May 27, 2025
Revised: June 28, 2025
Accepted: July 25, 2025
Published online: August 21, 2025
Processing time: 83 Days and 18.9 Hours
Abstract
BACKGROUND

Gastrointestinal diseases have complex etiologies and clinical presentations. An accurate diagnosis requires physicians to integrate diverse information, including medical history, laboratory test results, and imaging findings. Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information, resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.

AIM

To develop and evaluate a collaborative multimodal large language model (LLM) framework for clinical decision-making in digestive diseases.

METHODS

In this observational study, DeepGut, a multimodal LLM collaborative diagnostic framework, was developed to integrate four distinct large models into a four-tiered structure. The framework sequentially accomplishes multimodal information extraction, logical “chain” construction, diagnostic and treatment suggestion generation, and risk analysis. The model was evaluated using objective metrics, which assess the reliability and comprehensiveness of model-generated results, and subjective expert opinions, which examine the effectiveness of the framework in assisting physicians.

RESULTS

The diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance, with a diagnostic accuracy of 97.8%, diagnostic completeness of 93.9%, treatment plan accuracy of 95.2%, and treatment plan completeness of 98.0%, significantly surpassing the capabilities of single-modal LLM-based diagnostic tools. Experts evaluating the framework commended the completeness, relevance, and logical coherence of its outputs. However, the collaborative multimodal LLM approach resulted in increased input and output token counts, leading to higher computational costs and extended diagnostic times.

CONCLUSION

The framework achieves successful integration of multimodal diagnostic data, demonstrating enhanced performance enabled by multimodal LLM collaboration, which opens new horizons for the clinical application of artificial intelligence-assisted technology.

Keywords: Gastrointestinal diseases; Artificial intelligence-assisted diagnosis and treatment; Multimodal large language model; Multiple large language model collaboration; DeepGut

Core Tip: This study introduces DeepGut, a multimodal large language model (LLM) collaborative framework designed to assist in diagnostic processes by integrating multiple LLMs to extract and fuse multimodal clinical data such as medical history, laboratory tests, and imaging results. DeepGut significantly improves the diagnostic accuracy and comprehensiveness of gastrointestinal diseases compared with single-modal tools, as evidenced by expert validation. However, the framework’s higher token consumption by LLMs increases the operational costs, highlighting a key area for future optimization efforts.