From FIB-4 to the Artificial Intelligence Era: The Evolution of Non-Invasive Tools (NITs) for Tailored Risk Stratification in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)

Metabolic dysfunction–associated steatotic liver disease (MASLD) has become the leading global cause of chronic liver disease, driving cirrhosis, hepatic decompensation, hepatocellular carcinoma (HCC), and a disproportionate burden of major adverse cardiovascular events (MACEs). Fibrosis stage remains the strongest prognostic hepatic determinant, yet liver biopsy and Hepatic Venous Pressure Gradient (HVPG)—despite their diagnostic value—are invasive and unsuitable for population-level risk assessment. Over the past two decades, non-invasive tools (NITs), including serological scores, elastography-based techniques, and composite models, have transformed fibrosis and portal hypertension (PH) evaluation, though their accuracy declines in obese or comorbid patients and their ability to predict long-term hepatic and extrahepatic outcomes remains limited. These gaps have catalyzed the emergence of AI-driven, multimodal predictive systems capable of integrating biochemical, imaging, clinical, and histopathological data into individualized and dynamically updated risk profiles. This narrative review synthesizes current evidence on traditional and next-generation NITs, highlighting how AI-enhanced approaches may overcome long-standing diagnostic constraints and enable a unified assessment of hepatic and cardiometabolic risk. Collectively, these innovations outline a path toward precision hepatology, with the potential to reshape surveillance strategies, refine prognostic stratification, and improve outcomes across the full spectrum of MASLD.