Salman A. Artificial intelligence in autoimmune liver diseases: Diagnosis, risk stratification, and personalized care. World J Hepatol 2026; 18(10): 124997 [DOI: 10.4254/wjh.124997]
Corresponding Author of This Article
Ahmed Salman, FRACP, FRCP, Department of Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Egypt. awea844@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
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. Oct 27, 2026; 18(10): 124997 Published online Oct 27, 2026. doi: 10.4254/wjh.124997
Artificial intelligence in autoimmune liver diseases: Diagnosis, risk stratification, and personalized care
Ahmed Salman
Ahmed Salman, Department of Internal Medicine, Kasr Alainy School of Medicine, Cairo 11562, Egypt
Author contributions: Salman A conceived and designed the review, conducted the literature search, interpreted the evidence, drafted the manuscript, and critically revised it for important intellectual content.
AI contribution statement: OpenAI Codex (GPT-5) was used to assist with language editing, formatting, and literature organization. It was not used to generate research data, perform primary analyses, determine study eligibility, or make autonomous scientific interpretations or conclusions. The author verified the cited evidence, study characteristics, bibliographic metadata, interpretations, and final text and takes full responsibility for the manuscript’s accuracy, originality, and integrity.
Conflict-of-interest statement: The author declares no conflict of interest.
Corresponding author: Ahmed Salman, FRACP, FRCP, Department of Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 11562, Egypt. awea844@gmail.com
Received: June 29, 2026 Revised: August 5, 2026 Accepted: September 17, 2026 Published online: October 27, 2026 Processing time: 115 Days and 22.1 Hours
Core Tip
Core Tip: Artificial intelligence can be used to quantify images and combine multimodal data in autoimmune liver disease; however, most models remain investigational. The best-supported evidence currently comes from externally validated differential diagnosis models for autoimmune hepatitis, established machine learning prognostic tools for primary sclerosing cholangitis, and treatment response models for primary biliary cholangitis. However, these tools require local validation and monitoring during clinical testing. Clinicians should seek calibration, comparison with current care, decision curve benefit, and subgroup performance; however, the area under the curve alone is insufficient. No current model supports autonomous diagnosis or changes in therapy or surveillance without guideline-based clinical judgment.
