Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications

doi:10.35712/aig.124329

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Artificial Intelligence in Gastroenterology

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2644-3236

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

Artif Intell Gastroenterol. Nov 8, 2026; 7(3): 124329
Published online Nov 8, 2026. doi: 10.35712/aig.124329

Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications

Mona Mohamed Ibrahim Abdalla, Fabian Davamani, Nabil Eid

Mona Mohamed Ibrahim Abdalla, Nabil Eid, Department of Human Biology, School of Medicine, IMU University, Kuala Lumpur 57000, Malaysia

Fabian Davamani, Division of Applied Biomedical Sciences and Biotechnology, School of Health Sciences, IMU University, Kuala Lumpur 57000, Malaysia

Author contributions: Eid N conceived the review; Abdalla MMI, Davamani F, and Eid N drafted the manuscript; Abdalla MMI and Davamani F prepared the initial artwork; Eid N critically revised the manuscript. All authors reviewed and approved the final revised version.

AI contribution statement: OpenAI ChatGPT (GPT-5.6) was used solely for language editing and improvement. It was not used to generate research data or make scientific decisions. The authors reviewed and approved the final manuscript and remain responsible for all scientific content, references, interpretations, and conclusions.

Conflict-of-interest statement: The authors declare that they have no conflicts of interest.

Corresponding author: Nabil Eid, MD, PhD, Associate Professor, Department of Human Biology, School of Medicine, IMU University, Bukit Jalil, Kuala Lumpur 57000, Malaysia. nabilsaleheid@imu.edu.my

Received: June 15, 2026
Revised: August 4, 2026
Accepted: September 9, 2026
Published online: November 8, 2026
Processing time: 144 Days and 20.2 Hours

Core Tip

Core Tip: Artificial intelligence (AI) can integrate clinical records, radiological imaging, digital histology, and molecular data to support metabolic dysfunction-associated steatotic liver disease detection and risk stratification. Evidence is strongest for retrospective identification, imaging-based classification, and reproducible histological quantification. In contrast, AI-guided treatment selection, lifestyle personalization, and therapeutic monitoring remain investigational. This review distinguishes clinically proximate applications from exploratory and preclinical uses and identifies the validation, fairness, implementation, and outcome requirements for responsible clinical translation.