Abdalla MMI, Davamani F, Eid N. Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications. Artif Intell Gastroenterol 2026; 7(3): 124329 [DOI: 10.35712/aig.124329]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
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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/
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
Abstract
Metabolic dysfunction-associated steatotic liver disease is the most prevalent chronic liver disease worldwide and may progress from steatosis to metabolic dysfunction-associated steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma. Insulin resistance, obesity, mitochondrial dysfunction, inflammation, and impaired autophagy contribute to its pathogenesis. Early diagnosis, monitoring of disease progression, and assessment of therapeutic response remain important clinical challenges. Artificial intelligence (AI) and machine learning may improve disease prediction by integrating laboratory, metabolic, imaging, histological, and molecular data. Natural language processing and deep-learning approaches applied to radiological imaging and digital histopathology may support automated quantification of steatosis, inflammation, and fibrosis, while AI-assisted multi-omics analysis may identify molecular signatures relevant to disease stratification. Current evidence is strongest for retrospective case identification, imaging-based classification, and quantitative digital pathology; therapeutic selection and monitoring remain largely investigational. AI should therefore be viewed as a decision-support technology rather than a replacement for clinical judgment.
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.
