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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Dec 14, 2025; 31(46): 111176
Published online Dec 14, 2025. doi: 10.3748/wjg.v31.i46.111176
Published online Dec 14, 2025. doi: 10.3748/wjg.v31.i46.111176
Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models
Jing-Ran Sun, Bao-Cheng Deng, The Second Department of Infectious Diseases, The First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning Province, China
Jing-Ran Sun, Bing-Jiu Lu, Department of Hepatology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110032, Liaoning Province, China
Xiao-Ning Sun, Department of Geriatrics, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang 110032, Liaoning Province, China
Co-first authors: Jing-Ran Sun and Xiao-Ning Sun.
Co-corresponding authors: Bing-Jiu Lu and Bao-Cheng Deng.
Author contributions: Sun JR and Sun XN contributed equally to this work, they participated in the literature review, data collection, and manuscript writing; Lu BJ and Deng BC contributed equally to this work, they designed the draft and critically reviewed the manuscript for academic rigor. All authors have read and approved the final manuscript.
Supported by the Science Planning Project of Liaoning Province, No. 2019JH2/10300031-05; and the National Natural Science Foundation of China, No. 12171074.
Conflict-of-interest statement: All 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: Bao-Cheng Deng, PhD, The Second Department of Infectious Diseases, The First Affiliated Hospital, China Medical University, No. 155 Nanjing North Street, Shenyang 110001, Liaoning Province, China. sydengbc@163.com
Received: June 30, 2025
Revised: August 31, 2025
Accepted: October 21, 2025
Published online: December 14, 2025
Processing time: 165 Days and 14.8 Hours
Revised: August 31, 2025
Accepted: October 21, 2025
Published online: December 14, 2025
Processing time: 165 Days and 14.8 Hours
Core Tip
Core Tip: This review highlights how artificial intelligence is transforming hepatology by enabling early diagnosis, fibrosis staging, hepatocellular carcinoma detection, and personalized treatment. Key innovations include deep learning for imaging, multi-omics integration, and privacy-preserving federated learning. Explainable artificial intelligence builds clinician trust. Despite promising results, challenges like data heterogeneity, regulatory barriers, and limited real-time integration remain. Continued efforts in validation, ethical oversight, and user-centered design are essential for clinical adoption.