Suarez M, Martínez R, González-Martínez F, Torres AM, Mateo J. Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges. World J Hepatol 2026; 18(2): 114834 [DOI: 10.4254/wjh.v18.i2.114834]
Corresponding Author of This Article
Miguel Suarez, MD, PhD, Department of Gastroenterology, Virgen de la Luz Hospital, Hermandad Donantes de Sangre, 1, Cuenca 16002, Castille-La Mancha, Spain. msuarezmatias@sescam.jccm.es
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Gastroenterology & Hepatology
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Review
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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/
Feb 27, 2026 (publication date) through Feb 12, 2026
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Publication Name
World Journal of Hepatology
ISSN
1948-5182
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Suarez M, Martínez R, González-Martínez F, Torres AM, Mateo J. Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges. World J Hepatol 2026; 18(2): 114834 [DOI: 10.4254/wjh.v18.i2.114834]
World J Hepatol. Feb 27, 2026; 18(2): 114834 Published online Feb 27, 2026. doi: 10.4254/wjh.v18.i2.114834
Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges
Miguel Suarez, Raquel Martínez, Félix González-Martínez, Ana María Torres, Jorge Mateo
Miguel Suarez, Raquel Martínez, Department of Gastroenterology, Virgen de la Luz Hospital, Cuenca 16002, Castille-La Mancha, Spain
Miguel Suarez, Raquel Martínez, Félix González-Martínez, Ana María Torres, Jorge Mateo, Medical Analysis Expert Group, Universidad de Castilla-La Mancha, Cuenca 16071, Castille-La Mancha, Spain
Miguel Suarez, Raquel Martínez, Félix González-Martínez, Ana María Torres, Jorge Mateo, Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo 45071, Castille-La Mancha, Spain
Félix González-Martínez, Department of Emergency Medicine, Virgen de la Luz Hospital, Cuenca 16002, Castille-La Mancha, Spain
Author contributions: Suárez M, Martínez R, González-Martínez F, Torres AM and Mateo J participated in the design, editing and data collection of the manuscript; Suárez M, Martínez R, and Mateo J contributed to the review of the literature and writing. All authors have reviewed and approved the paper.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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: Miguel Suarez, MD, PhD, Department of Gastroenterology, Virgen de la Luz Hospital, Hermandad Donantes de Sangre, 1, Cuenca 16002, Castille-La Mancha, Spain. msuarezmatias@sescam.jccm.es
Received: September 29, 2025 Revised: November 17, 2025 Accepted: December 18, 2025 Published online: February 27, 2026 Processing time: 136 Days and 15.2 Hours
Core Tip
Core Tip: Artificial intelligence (AI) is rapidly transforming gastroenterology and hepatology, offering tools that enhance diagnostic precision, personalize treatment, and reduce variability in clinical practice. Applications such as computer-aided detection in colonoscopy, non-invasive assessment of liver fibrosis, and predictive modeling in inflammatory bowel disease illustrate its tangible clinical impact. However, the widespread adoption of AI remains constrained by methodological barriers, data quality issues, lack of external validation, and ethical challenges related to transparency, accountability, and equitable access. This review provides a comprehensive overview of benefits, limitations, and future perspectives, emphasizing the importance of responsible and patient-centered integration of AI.
