Boutos P, Karakasi KE, Katsanos G, Antoniadis N, Kofinas A, Tsoulfas G. Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives. World J Hepatol 2026; 18(1): 111902 [DOI: 10.4254/wjh.v18.i1.111902]
Corresponding Author of This Article
Panagiotis Boutos, Department of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Konstantinoupoleos 49, Thessaloniki 54642, Kentrikí Makedonía, Greece. pgmpoutos@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
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/
Jan 27, 2026 (publication date) through Jan 27, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Hepatology
ISSN
1948-5182
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Boutos P, Karakasi KE, Katsanos G, Antoniadis N, Kofinas A, Tsoulfas G. Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives. World J Hepatol 2026; 18(1): 111902 [DOI: 10.4254/wjh.v18.i1.111902]
Panagiotis Boutos, Georgios Katsanos, Nikolaos Antoniadis, Georgios Tsoulfas, Department of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Thessaloniki 54642, Kentrikí Makedonía, Greece
Konstantina-Eleni Karakasi, Athanasios Kofinas, Department of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University of Thessaloniki, Thessaloniki 54642, Kentrikí Makedonía, Greece
Co-corresponding authors: Panagiotis Boutos and Georgios Tsoulfas.
Author contributions: Boutos P and Tsoulfas G conceived the study concept and supervised the overall research, they contributed equally to this article, they are the co-corresponding authors of this manuscript; Boutos P designed the methodology, conducted the primary analysis, and drafted the initial manuscript; Karakasi KE contributed to data collection and assisted in literature review; Katsanos G and Antoniadis N participated in statistical analysis and interpretation of results; Kofinas A contributed to data curation and critical revisions of the manuscript; Tsoulfas G provided senior supervision, critical intellectual input, and final approval of the manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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/
Corresponding author: Panagiotis Boutos, Department of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Konstantinoupoleos 49, Thessaloniki 54642, Kentrikí Makedonía, Greece. pgmpoutos@gmail.com
Received: July 14, 2025 Revised: August 29, 2025 Accepted: December 2, 2025 Published online: January 27, 2026 Processing time: 198 Days and 17.5 Hours
Abstract
Artificial intelligence (AI) has made remarkable strides, becoming an essential tool in modern medicine. As AI continues to evolve, it is crucial to redefine its scope, classifications, and subtypes to better align with its clinical applications and potential. With a growing number of sophisticated models, AI is now widely used in gastroenterology and hepatology, offering new ways to enhance patient care. In gastroenterology, AI helps doctors identify lesions during endoscopy, detect gastrointestinal bleeding, and support the diagnosis and treatment of conditions like inflammatory bowel disease and gastrointestinal cancers. In hepatology, it aids in staging liver fibrosis, tracking disease progression, and predicting hepatocellular carcinoma risks. Machine learning further personalizes treatment plans, helping physicians make more informed decisions. However, despite its promise, AI still faces hurdles, including biases in data, ethical considerations, regulatory challenges, and the need for better transparency. Moving forward, refining these models, conducting extensive validation studies, and integrating AI seamlessly into clinical practice will be crucial in fully realizing its benefits for gastroenterology and hepatology.
Core Tip: This article presents a structured overview of artificial intelligence (AI) in gastroenterology and hepatology, integrating fundamental concepts with real-world clinical applications. By aligning AI tools with each stage of the patient journey, we highlight how AI can enhance prevention, diagnosis, treatment, and follow-up. We also discuss challenges related to trustworthiness, including interpretability, generalisability, and ethics. Through practical examples and original visual frameworks, this article aims to guide clinicians and researchers in understanding, evaluating, and responsibly implementing AI technologies in digestive and liver healthcare.
