Wu YM, Tang FY, Qi ZX. Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges. World J Gastroenterol 2025; 31(38): 109802 [DOI: 10.3748/wjg.v31.i38.109802]
Corresponding Author of This Article
Yi-Mao Wu, Academic Fellow, The Second Clinical Medical College, Guangdong Medical University, No. 1 Xincheng Avenue, Songshan Lake District, Dongguan 523808, Guangdong Province, China. wuyimao_doctor@gdmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Oct 14, 2025; 31(38): 109802 Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.109802
Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges
Yi-Mao Wu, Fei-Yang Tang, Zi-Xin Qi
Yi-Mao Wu, Fei-Yang Tang, The Second Clinical Medical College, Guangdong Medical University, Dongguan 523808, Guangdong Province, China
Zi-Xin Qi, College of Pharmacy, Guangdong Medical University, Dongguan 523808, Guangdong Province, China
Co-first authors: Yi-Mao Wu and Fei-Yang Tang.
Author contributions: Wu YM, Tang FY and Qi ZX contributed to searching, analyzing, writing-original draft preparation; Wu YM and Tang FY contributed to writing, reviewing; Wu YM and Qi ZX contributed to reviewing, editing; Wu YM contributed to reviewing, editing and supervision; All authors approved the final version of the manuscript.
Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.
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: Yi-Mao Wu, Academic Fellow, The Second Clinical Medical College, Guangdong Medical University, No. 1 Xincheng Avenue, Songshan Lake District, Dongguan 523808, Guangdong Province, China. wuyimao_doctor@gdmu.edu.cn
Received: May 22, 2025 Revised: June 22, 2025 Accepted: August 26, 2025 Published online: October 14, 2025 Processing time: 145 Days and 18.3 Hours
Abstract
With the rapid development of artificial intelligence (AI) technology, multimodal data integration has become an important means to improve the accuracy of diagnosis and treatment in gastroenterology and hepatology. This article systematically reviews the latest progress of multimodal AI technology in the diagnosis, treatment, and decision-making for gastrointestinal tumors, functional gastrointestinal diseases, and liver diseases, focusing on the innovative applications of endoscopic image AI, pathological section AI, multi-omics data fusion models, and wearable devices combined with natural language processing. Multimodal AI can significantly improve the accuracy of early diagnosis and the efficiency of individualized treatment planning by integrating imaging, pathological data, molecular, and clinical phenotypic data. However, current AI technologies still face challenges such as insufficient data standardization, limited generalization of models, and ethical compliance. This paper proposes solutions, such as the establishment of cross-center data sharing platform, the development of federated learning framework, and the formulation of ethical norms, and looks forward to the application prospect of multimodal large-scale models in the disease management process. This review provides theoretical basis and practical guidance for promoting the clinical translation of AI technology in the field of gastroenterology and hepatology.
Core Tip: This manuscript systematically reviews the latest progress in multimodal artificial intelligence (AI) in gastroenterology and hepatology, focusing on innovative applications, including endoscopic AI (89% sensitivity for early gastric cancer detection), multi-omics models (42% objective response rate for programmed cell death 1-sensitive gastric cancer subtypes), magnetic resonance imaging-based liver fibrosis staging [area under the curve (AUC) = 0.89], and AI-driven hepatocellular carcinoma recurrence prediction (AUC = 0.91). It analyzes critical challenges (data standardization gaps, model “black boxes”) and proposes solutions (federated learning, interpretable frameworks), providing vital guidance for AI’s clinical translation to advance precision medicine.
