Lou JJ, Zeng J. Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects. World J Gastroenterol 2025; 31(47): 111900 [DOI: 10.3748/wjg.v31.i47.111900]
Corresponding Author of This Article
Jing Zeng, MD, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai 200092, China. zjupup@126.com
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Gastroenterology & Hepatology
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Minireviews
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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/
Dec 21, 2025 (publication date) through Dec 19, 2025
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World Journal of Gastroenterology
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1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Lou JJ, Zeng J. Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects. World J Gastroenterol 2025; 31(47): 111900 [DOI: 10.3748/wjg.v31.i47.111900]
World J Gastroenterol. Dec 21, 2025; 31(47): 111900 Published online Dec 21, 2025. doi: 10.3748/wjg.v31.i47.111900
Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects
Jian-Jun Lou, Jing Zeng
Jian-Jun Lou, Chronic Liver Disease Center, The Affiliated Yangming Hospital of Ningbo University (Yuyao People's Hospital), Ningbo 315400, Zhejiang Province, China
Jing Zeng, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
Author contributions: Lou JJ designed the overall concept and outline of the manuscript; Lou JJ and Zeng J contributed to this paper, the writing, and editing the manuscript.
Supported by Shanghai Pujiang Program, No. 24PJD071; National Natural Science Foundation of China, No. 82100605; and Star Program of Shanghai Jiao Tong University, No. YG2021QN54.
Conflict-of-interest statement: The authors declare no competing financial interests.
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: Jing Zeng, MD, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai 200092, China. zjupup@126.com
Received: July 14, 2025 Revised: October 7, 2025 Accepted: November 5, 2025 Published online: December 21, 2025 Processing time: 160 Days and 9.9 Hours
Abstract
The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) have continued to increase in recent years, making it one of the most common chronic liver diseases worldwide. MASLD is highly comorbid with obesity, type 2 diabetes, cardiovascular disease, and chronic kidney disease, posing a serious threat to public health and creating a significant medical and socioeconomic burden. Despite advances in research, current clinical practice still faces considerable challenges in early screening, risk stratification, prognostic prediction, and long-term therapeutic monitoring. Recent advances in artificial intelligence (AI) have provided transformative opportunities to address these challenges. AI has demonstrated unique advantages in imaging interpretation, multiomics integration, electronic health record analysis, and remote health management, significantly improving the accuracy and efficiency of the noninvasive diagnosis, individualized risk stratification, precision therapy, and dynamic disease monitoring of MASLD. In this mini-review, the latest advances in AI applications for MASLD diagnosis and management are systematically summarized, and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered, with the aim of providing theoretical and practical guidance for the clinical management of this disease.
Core Tip: Artificial intelligence (AI) is rapidly advancing the management of metabolic dysfunction-associated steatotic liver disease (MASLD). Because AI can integrate imaging, multiomics, and electronic health record data, the use of AI enables earlier diagnosis, more accurate risk stratification, and personalized treatment. This mini-review summarizes recent progress and future directions for applying AI to improve MASLD care and outcomes.
