Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence
Year: 2025
Type: article
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.
Authors: Yu-Ning Gao, Mei-Ling Chen, Wen-Mao Li, Qing Liu, Yan Jiao
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411101313