Chen ZL, Wang C, Wang F. Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice. World J Gastroenterol 2025; 31(24): 108021 [DOI: 10.3748/wjg.v31.i24.108021]
Corresponding Author of This Article
Fang Wang, Doctor, Professor, Department of Pathogen Biology, College of Basic Medical Sciences, Jilin University, No. 126 Xinmin Street, Changchun 130021, Jilin Province, China. wf@jlu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
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. Jun 28, 2025; 31(24): 108021 Published online Jun 28, 2025. doi: 10.3748/wjg.v31.i24.108021
Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice
Zhi-Li Chen, Chao Wang, Fang Wang
Zhi-Li Chen, Chao Wang, Fang Wang, Department of Pathogen Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, Jilin Province, China
Zhi-Li Chen, Chao Wang, Fang Wang, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Jilin University, Changchun 130021, Jilin Province, China
Chao Wang, Fang Wang, Jilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases, Jilin University, Changchun 130021, Jilin Province, China
Co-first authors: Zhi-Li Chen and Chao Wang.
Author contributions: Chen ZL and Wang C contributed equally to this article; Chen ZL reviewed the literature and wrote the first draft of the paper; Wang C contributed to searching the literature and edited it extensively; Wang F conceived the idea and edited it; All authors have read and approved the final version.
Supported by the Natural Science Foundation of Jilin Province, No. YDZJ202401182ZYTS; Jilin Provincial Key Laboratory of Precision Infectious Diseases, No. 20200601011JC; and Jilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases, Jilin Province Development and Reform Commission, No. 2022C036.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Fang Wang, Doctor, Professor, Department of Pathogen Biology, College of Basic Medical Sciences, Jilin University, No. 126 Xinmin Street, Changchun 130021, Jilin Province, China. wf@jlu.edu.cn
Received: April 3, 2025 Revised: April 21, 2025 Accepted: June 4, 2025 Published online: June 28, 2025 Processing time: 84 Days and 18.5 Hours
Abstract
Artificial intelligence (AI) is driving a paradigm shift in gastroenterology and hepatology by delivering cutting-edge tools for disease screening, diagnosis, treatment, and prognostic management. Through deep learning, radiomics, and multimodal data integration, AI has achieved diagnostic parity with expert clinicians in endoscopic image analysis (e.g., early gastric cancer detection, colorectal polyp identification) and non-invasive assessment of liver pathologies (e.g., fibrosis staging, fatty liver typing) while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and optimizing inflammatory bowel disease treatment responses. Despite these advancements challenges persist including limited model generalization due to fragmented datasets, algorithmic limitations in rare conditions (e.g., pediatric liver diseases) caused by insufficient training data, and unresolved ethical issues related to bias, accountability, and patient privacy. Mitigation strategies involve constructing standardized multicenter databases, validating AI tools through prospective trials, leveraging federated learning to address data scarcity, and developing interpretable systems (e.g., attention heatmap visualization) to enhance clinical trust. Integrating generative AI, digital twin technologies, and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders, improve global health outcomes, and reshape healthcare equity.
Core Tip: This review highlights artificial intelligence (AI)-driven innovations in gastroenterology and hepatology, demonstrating breakthroughs in endoscopic/image analysis, multi-omics integration, and precision therapy. AI achieves diagnostic parity with experts in detecting early cancers and fibrosis, while addressing challenges like data fragmentation and bias through standardized databases, federated learning, and explainable systems. The study emphasizes interdisciplinary collaboration and ethical frameworks to advance equitable healthcare access and redefine digestive disease management.
