Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Hepatol. May 27, 2026; 18(5): 117141
Published online May 27, 2026. doi: 10.4254/wjh.v18.i5.117141
Published online May 27, 2026. doi: 10.4254/wjh.v18.i5.117141
Artificial intelligence and machine learning in hepatology: Revolutionizing diagnosis and treatment
Mohammed OK Elsayed, Ahmed Y Elshabrawi, Department of Gastroenterology, South Tees Hospitals NHS Foundation Trust, The James Cook University Hospital, Middlesbrough TS4 3BW, United Kingdom
Ahmed Y Elshabrawi, Department of Endemic Hepatology and Gastroenterology, Mansoura University, Mansoura 35516, Egypt
Co-first authors: Mohammed OK Elsayed and Ahmed Y Elshabrawi.
Author contributions: Both authors contributed significantly to the preparation of this review article; Elsayed MOK conceptualized the scope of the review, supervised the work, and provided critical revisions; Elshabrawi AY conducted the literature search, synthesized the evidence, and drafted the initial manuscript; Elsayed MOK and Elshabrawi AY reviewed, edited, and approved the final version of the manuscript.
AI contribution statement: The authors used a large language model (ChatGPT, OpenAI, Grammarly) to only assist with language editing and refinement. All scientific content was critically reviewed and verified by the authors, who take full responsibility for the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Mohammed OK Elsayed, MD, Professor, Department of Gastro
Received: December 1, 2025
Revised: January 4, 2026
Accepted: January 29, 2026
Published online: May 27, 2026
Processing time: 178 Days and 5.9 Hours
Revised: January 4, 2026
Accepted: January 29, 2026
Published online: May 27, 2026
Processing time: 178 Days and 5.9 Hours
Core Tip
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.