Elsayed MO, Elshabrawi AY. Artificial intelligence and machine learning in hepatology: Revolutionizing diagnosis and treatment. World J Hepatol 2026; 18(5): 117141 [DOI: 10.4254/wjh.v18.i5.117141]
Corresponding Author of This Article
Mohammed OK Elsayed, 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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
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/
World J Hepatol. May 27, 2026; 18(5): 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
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 Gastroenterology, South Tees Hospitals NHS Foundation Trust, The James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, United Kingdom. mohammed.omar@nhs.net
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
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in the field of hepatology, offering new opportunities to enhance diagnosis, risk stratification, and therapeutic decision-making. AI models have demonstrated improved performance compared with conventional methods by integrating complex multimodal datasets. These models have shown promising results across a broad spectrum of liver disease management. Despite these advances, unmet needs remain a significant barrier to the full integration of AI models into clinical practice. Future progress will depend on developing interpretable, well-validated AI systems supported by multicentre collaborations and robust data infrastructure. In this comprehensive review, we summarise the current applications of AI in hepatology, highlight areas of significant clinical promise, and outline the challenges and future directions necessary for safe, equitable, and effective integration into routine practice.
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.
