Publication Name	World Journal of Hepatology
Manuscript ID	117141
Country	United Kingdom

Category	Gastroenterology & Hepatology
Manuscript Type	Review
Article Title	Artificial intelligence and machine learning in hepatology: Revolutionizing diagnosis and treatment
Manuscript Source	Invited Manuscript
All Author List	Mohammed OK Elsayed and Ahmed Y Elshabrawi
Funding Agency and Grant Number
Corresponding Author	Mohammed OK Elsayed, FRCP, MD, Professor, Department of Gastroenterology, South Tees Hospitals NHS Foundation Trust, The James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, United Kingdom. mohammed.omar@nhs.net
Key Words	Artificial intelligence; Machine learning; Deep learning; Liver transplant; Hepatocellular carcinoma; Cirrhosis
Core Tip	Artificial intelligence (AI) is transforming hepatology by improving disease detection, prognostication, and treatment planning. Machine learning and deep learning models outperform traditional tools by integrating imaging, laboratory, histological, and clinical data. However, challenges, including dataset bias, lack of interpretability, and limited prospective validation, still hinder routine clinical use. This review outlines current applications, limitations, and future directions, highlighting how AI could support more precise, equitable, and personalized liver care.
Citation	Elsayed MOK, Elshabrawi AY. Artificial intelligence and machine learning in hepatology: Revolutionizing diagnosis and treatment. World J Hepatol 2026; In press

Received	2025-12-01 00:42
Peer-Review Started	2025-12-01 00:42
First Decision by Editorial Office Director	2025-12-25 08:24
Return for Revision	2025-12-25 08:24
Revised	2026-01-04 21:49
Publication Fee Transferred
Second Decision by Editor	2026-01-29 02:33
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-29 08:06
Articles in Press	2026-01-29 08:06
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5182 (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: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
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