©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 14, 2026; 32(2): 111737
Published online Jan 14, 2026. doi: 10.3748/wjg.v32.i2.111737
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, 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.
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 18.2 Hours
Revised: September 6, 2025
Accepted: November 24, 2025
Published online: January 14, 2026
Processing time: 188 Days and 18.2 Hours
Core Tip
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.