Gao YN, Chen ML, Li WM, Liu Q, Jiao Y. Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence. Artif Intell Gastroenterol 2025; 6(1): 107105 [DOI: 10.35712/aig.v6.i1.107105]
Corresponding Author of This Article
Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. lagelangri1@126.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/
Artif Intell Gastroenterol. Jun 8, 2025; 6(1): 107105 Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.107105
Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence
Yu-Ning Gao, Mei-Ling Chen, Wen-Mao Li, Qing Liu, Yan Jiao
Yu-Ning Gao, Department of Gastrointestinal Surgery, Changchun Central Hospital, Changchun 130012, Jilin Province, China
Mei-Ling Chen, School of Nursing, Jilin University, Changchun 130021, Jilin Province, China
Wen-Mao Li, Department of Rehabilitation Medicine, The Second Hospital of Jilin University, Changchun 130000, Jilin Province, China
Qing Liu, Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China
Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Author contributions: Gao YN, Chen ML, and Li WM contributed to the writing and editing of the manuscript; Jiao Y contributed to the discussion and design of the manuscript; Liu Q contributed to the literature search; Jiao Y designed the overall concept and outline of the manuscript; all authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. lagelangri1@126.com
Received: March 16, 2025 Revised: April 3, 2025 Accepted: April 16, 2025 Published online: June 8, 2025 Processing time: 83 Days and 8 Hours
Abstract
Metabolic-associated steatotic liver disease (MASLD), formerly referred to as non-alcoholic fatty liver disease, represents an escalating worldwide medical burden defined by hepatic steatosis, inflammation, fibrosis, and potential progression to cirrhosis or hepatocellular carcinoma. Artificial intelligence (AI) has emerged as a transformative tool in MASLD management, enhancing diagnostic accuracy, risk stratification, and treatment optimization. This review explores the integration of AI in MASLD diagnosis, including AI-based histopathological assessment, non-invasive screening models, imaging diagnostics, and gut microbiota-based approaches. Additionally, AI-driven treatment strategies facilitate personalized management, assess therapeutic response, and contribute to drug discovery. Despite its advantages, challenges such as data integration, model interpretability, and cost-effectiveness remain obstacles to widespread adoption. Future advancements in explainable AI, multi-modal data fusion, and cost-efficient implementations will be crucial for maximizing AI’s impact on MASLD care. AI-driven innovations hold great promise for improving early detection, guiding personalized treatment, and ultimately enhancing patient outcomes in MASLD.
Core Tip: Artificial intelligence (AI) is revolutionizing the diagnosis and treatment of metabolic-associated steatotic liver disease (MASLD) by enhancing diagnostic accuracy, enabling non-invasive assessments, and guiding personalized treatment strategies. Despite challenges such as data integration, model interpretability, and cost-effectiveness, AI has the potential to improve early detection, facilitate targeted interventions, and ultimately enhance patient outcomes in MASLD care.
