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It is highly comorbid with obesity, type 2 diabetes, cardiovascular disease, and chronic kidney disease, increasing medical and socioeconomic burden.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which clinical problems are still challenging in MASLD management?",{"text":68,"@type":64},"Current practice faces major difficulties in early screening, risk stratification, prognostic prediction, and long-term therapeutic monitoring.",{"name":70,"@type":61,"acceptedAnswer":71},"How can artificial intelligence help in MASLD diagnosis and management?",{"text":72,"@type":64},"AI can improve imaging interpretation, multiomics integration, electronic health record analysis, and remote health management. 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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.  \nKey Words: Metabolic dysfunction-associated steatotic liver disease; Artificial intelligence; Multiomics integration; Early screening; Risk stratification; Disease monitoring; Machine learning; Clinical decision support  \n WJG  [https://www.wjgnet.com](https://www.wjgnet.com) 1 December 21, 2025  Volume 31  Issue 47   \nLou JJ et al. AI in MASLD  \n©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.  \nCore 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.  \nCitation: Lou JJ, Zeng ","cbCaimjIlhQx8gxd","https://ap.wps.com/l/cbCaimjIlhQx8gxd","pdf",1210801,"English","# Abstract\n# Key Words\n# Core Tip\n# Citation\n# Introduction","[{\"question\":\"What makes MASLD a major public health concern?\",\"answer\":\"MASLD incidence and prevalence keep rising worldwide. It is highly comorbid with obesity, type 2 diabetes, cardiovascular disease, and chronic kidney disease, increasing medical and socioeconomic burden.\"},{\"question\":\"Which clinical problems are still challenging in MASLD management?\",\"answer\":\"Current practice faces major difficulties in early screening, risk stratification, prognostic prediction, and long-term therapeutic monitoring.\"},{\"question\":\"How can artificial intelligence help in MASLD diagnosis and management?\",\"answer\":\"AI can improve imaging interpretation, multiomics integration, electronic health record analysis, and remote health management. It supports earlier noninvasive diagnosis, more accurate risk stratification, precision therapy, and dynamic disease monitoring.\"}]","Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease - Current status and future prospects | PDF",1790565163,25]