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 [PMID: 41112005 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
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Gastroenterology & Hepatology
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Minireviews
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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/
Oct 14, 2025 (publication date) through Nov 23, 2025
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Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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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 [PMID: 41112005 DOI: 10.3748/wjg.v31.i38.109802]
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 18.5 Hours
Core Tip
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.
