Hernández-Almonacid PG, Marín-Quintero X. Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches. World J Gastroenterol 2026; 32(2): 111737 [DOI: 10.3748/wjg.v32.i2.111737]
Corresponding Author of This Article
Pablo Guillermo Hernández-Almonacid, MD, Consultant, Department of Internal Medicine, National University of Colombia, Kr 35 bis 60-45 A311, Bogota 111311, Colombia. pghernandezalm@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/
World J Gastroenterol. Jan 14, 2026; 32(2): 111737 Published online Jan 14, 2026. doi: 10.3748/wjg.v32.i2.111737
Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches
Pablo Guillermo Hernández-Almonacid, Ximena Marín-Quintero
Pablo Guillermo Hernández-Almonacid, Department of Internal Medicine, National University of Colombia, Bogota 111311, Colombia
Ximena Marín-Quintero, Department of Anatomical and Clinical Pathology, National University of Colombia, Bogota 111311, Colombia
Author contributions: Hernández-Almonacid PG was primarily responsible for manuscript writing, literature review, and the preparation of tables and figures; Marín-Quintero X contributed to the writing process and assisted with the literature search; and all authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Pablo Guillermo Hernández-Almonacid, MD, Consultant, Department of Internal Medicine, National University of Colombia, Kr 35 bis 60-45 A311, Bogota 111311, Colombia. pghernandezalm@gmail.com
Received: July 8, 2025 Revised: September 6, 2025 Accepted: November 24, 2025 Published online: January 14, 2026 Processing time: 188 Days and 14.6 Hours
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events. Given its significant clinical impact, the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes. Over the past decade, the integration of artificial intelligence (AI) into gastroenterology has led to transformative advancements in medical practice. AI represents a major step towards personalized medicine, offering the potential to enhance diagnostic accuracy, refine prognostic assessments, and optimize treatment strategies. Its applications are rapidly expanding. This article explores the emerging role of AI in the management of MASLD, emphasizing its ability to improve clinical prediction, enhance the diagnostic performance of imaging modalities, and support histopathological confirmation. Additionally, it examines the development of AI-guided personalized treatments, where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
Core Tip: Artificial intelligence (AI) is redefining the clinical approach to metabolic dysfunction-associated steatotic liver disease (MASLD). In diagnosis, it enhances the detection of steatosis and fibrosis beyond the limits of conventional tools. For prognosis, AI accurately stratifies risk and anticipates complications, consistently demonstrating superior performance. In treatment, it enables personalized interventions and accelerates drug development. By integrating multimodal data, including clinical, imaging, histopathological, and molecular information, AI transforms fragmented data into actionable insights, establishing itself as a cornerstone for the future of MASLD management.
