Artificial Intelligence in Metabolic Dysfunction-Associated Steatotic Liver Disease: A 3-Level Framework Across Clinical, Health System, and Policy Decisions

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent worldwide, yet most affected individuals remain undiagnosed, and pathways for noninvasive risk stratification and linkage to specialty care remain inconsistently implemented. With the emergence of pharmacologic therapies for metabolic dysfunction-associated steatohepatitis, accurate patient identification, disease staging, treatment selection, and longitudinal monitoring have become increasingly consequential. In this narrative review, we examine the evolving role of artificial intelligence (AI) across 3 levels of MASLD care, defined by where an AI output informs a decision and who acts on it: the individual patient, the health system, and health policy. At the patient level, machine-learning and deep-learning approaches have demonstrated potential applications in fibrosis assessment, histopathologic and imaging interpretation, multiomics integration, treatment-response prediction, and hepatocellular carcinoma risk stratification. Across selected datasets, several models have performed comparably to or better than conventional noninvasive tests and expert interpretation, although no validated tool currently predicts treatment response before therapy. At the health-system level, AI may support population-level case finding, extraction of clinically relevant information from unstructured health records, prognostic assessment, and more efficient allocation of confirmatory testing and specialty care. At the policy level, AI-informed disease-burden modeling and risk stratification may help guide decisions regarding workforce capacity, resource allocation, reimbursement, and treatment coverage. However, most available studies are retrospective, frequently originate from single centers, and have limited external validation. No randomized trial has yet demonstrated that AI-guided management improves clinical outcomes in MASLD, whereas governance, transparency, and equity frameworks remain underdeveloped. Realizing the clinical value of AI will require prospective multicenter studies embedded within clearly defined MASLD care decisions, validation across genetically and socioeconomically diverse populations and health system settings, standardized reporting and risk-of-bias assessment, and sustained attention to implementation, governance, and equity.