Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	107193
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications
Manuscript Source	Invited Manuscript
All Author List	Mei-Ling Chen, Yan Jiao, Ye-Hui Fan and Ya-Hui Liu
Funding Agency and Grant Number
Corresponding Author	Ya-Hui Liu, 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
Key Words	Alcohol-related liver disease; Artificial intelligence; Machine learning; Multi-omics data; Non-invasive biomarkers
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.
Citation	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; In press

Received	2025-03-18 02:49
Peer-Review Started	2025-03-18 02:49
To Make the First Decision
Return for Revision	2025-03-26 02:34
Revised	2025-04-04 15:44
Second Decision	2025-04-18 02:45
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-18 08:04
Articles in Press	2025-04-18 08:04
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Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2644-3236 (online)
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: http://creativecommons.org/licenses/by-nc/4.0/
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