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World J Gastroenterol. Oct 21, 2025; 31(39): 111323
Published online Oct 21, 2025. doi: 10.3748/wjg.v31.i39.111323
Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine
Ying Zheng, Han Li, Ru Wang, Cong-Shan Jiang, Yi-Tong Zhao
Ying Zheng, Key Laboratory for High Altitude Brain Injury and Repair, School of Medicine, Xizang Minzu University, Xianyang 712082, Shaanxi Province, China
Ying Zheng, Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China
Ying Zheng, Shaanxi Key Laboratory of Gastrointestinal Motility Disorders, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China
Ying Zheng, Shaanxi Provincial Clinical Research Center for Gastrointestinal Diseases, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China
Han Li, Department of Plastic, Aesthetic and Maxillofacial Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
Ru Wang, Department of Minimally Invasive Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
Cong-Shan Jiang, Yi-Tong Zhao, Shaanxi Institute for Pediatric Diseases, Key Laboratory of Precision Medicine to Pediatric Diseases of Shaanxi Province, Xi’an Children’s Hospital, Affiliated Children’s Hospital of Xi’an Jiaotong University, Xi’an 710003, Shaanxi Province, China
Author contributions: Zheng Y contributes to paper writing; Li H and Wang R contribute to literature review; Jiang CS is responsible for the proofreading of English writing; Zhao YT designed the subject of the article and proofreading. All authors approved the final version to publish.
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: Yi-Tong Zhao, PhD, Shaanxi Institute for Pediatric Diseases, Key Laboratory of Precision Medicine to Pediatric Diseases of Shaanxi Province, Xi’an Children’s Hospital, Affiliated Children’s Hospital of Xi’an Jiaotong University, No. 69 Xijuyuan Lane, Lianhu District, Xi’an 710003, Shaanxi Province, China. xjmzyt@163.com
Received: June 30, 2025
Revised: July 29, 2025
Accepted: September 12, 2025
Published online: October 21, 2025
Processing time: 116 Days and 4.9 Hours
Core Tip

Core Tip: This minireview highlights artificial intelligence innovations in hepatology. It develops multi-modal data fusion strategies integrating imaging, genomics, lab results, and clinical records via transformer models and graph neural networks to uncover latent associations and build disease knowledge graphs. Convolutional neural networks enable automated, high-accuracy liver lesion detection and quantification in pathology/imaging. Artificial intelligence optimizes personalized antiviral therapy and liver transplant outcomes through drug-response prediction and graft-recipient matching. Natural language processing extracts critical insights from electronic health records. These approaches significantly advance diagnosis, treatment personalization, and translational research in precision hepatology.