Chen ML, Jiao Y, Fan YH, Liu YH. Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications. Artif Intell Gastroenterol 2025; 6(1): 107193 [DOI: 10.35712/aig.v6.i1.107193]
Corresponding Author of This Article
Ya-Hui Liu, Professor, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Xinmin Street, Changchun 130021, Jilin Province, China. yahui@jlu.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/
Artif Intell Gastroenterol. Jun 8, 2025; 6(1): 107193 Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.107193
Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications
Mei-Ling Chen, Yan Jiao, Ye-Hui Fan, Ya-Hui Liu
Mei-Ling Chen, School of Nursing, Jilin University, Changchun 130021, Jilin Province, China
Yan Jiao, Ya-Hui Liu, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Ye-Hui Fan, Department of The First Operation Room, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Co-corresponding authors: Ye-Hui Fan and Ya-Hui Liu.
Author contributions: Liu YH contributed to the writing, editing of the manuscript and table; Fan YH contributed to the discussion and design of the manuscript; Jiao Y contributed to the literature search; Chen ML, Jiao Y designed the overall concept and outline of the manuscript. All authors have read and approve the final manuscript. Liu YH spearheaded the structural development and scholarly refinement of the manuscript. He orchestrated the critical revision process, ensuring methodological rigor and logical coherence across all sections. As co-corresponding author, he assumed responsibility for cross-team communication, addressing reviewers' technical inquiries, and finalizing the submission-ready version of the manuscript. Fan YH provided strategic leadership in shaping the manuscript's scientific narrative and theoretical framework. He designed the innovative conceptual architecture for the discussion section, integrating clinical implications with fundamental mechanistic insights. As co-corresponding author, Fan YH coordinated multi-institutional collaborations and supervised the translational interpretation of data. His dual role encompassed both high-level academic mentorship and hands-on troubleshooting during the peer review process.
Conflict-of-interest statement: All the authors report having 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: Ya-Hui Liu, Professor, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Xinmin Street, Changchun 130021, Jilin Province, China. yahui@jlu.edu.cn
Received: March 18, 2025 Revised: April 4, 2025 Accepted: April 18, 2025 Published online: June 8, 2025 Processing time: 81 Days and 0.3 Hours
Abstract
Alcohol-related liver disease (ARLD) remains a major public health concern, often diagnosed at advanced stages with limited treatment options. Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes. Artificial intelligence (AI) has emerged as a powerful tool for predicting ARLD, leveraging multi-omics data, machine learning algorithms, and non-invasive biomarkers. This review explores the current advancements in AI-driven ARLD prediction, highlighting key methodologies such as multi-omics data integration, gut microbiome-based modeling, and predictive analytics using machine learning techniques. AI models incorporating transcriptomics, proteomics, and clinical data have demonstrated high diagnostic accuracy, with some achieving an area under the curve exceeding 0.90. Furthermore, non-invasive biomarkers, including liver stiffness measurements and circulating proteomic panels, have been successfully integrated into AI frameworks for early detection and risk stratification. Despite these advancements, challenges such as data heterogeneity, model generalizability, and ethical considerations remain. Future directions include the development of advanced biomarker discovery, wearable and point-of-care AI-integrated technologies, and precision medicine approaches tailored to individual risk profiles. AI-driven models hold significant potential in transforming ARLD prediction and management, ultimately contributing to early diagnosis and improved clinical outcomes.
Core Tip: Artificial intelligence (AI) has emerged as a transformative tool for early prediction of alcohol-related liver disease (ARLD). By integrating multi-omics data, gut microbiome analysis, and machine learning algorithms, AI models have achieved high diagnostic accuracy and predictive capability. This review explores key studies, methodologies, and clinical applications of AI in ARLD prediction, addressing challenges such as data heterogeneity and model generalizability. The future of AI in ARLD lies in advanced biomarker discovery, wearable technology, and personalized medicine approaches.
