Abstract
Artificial intelligence (AI) is rapidly transforming the landscape of hepatology by enabling automated data interpretation, early disease detection, and individualized treatment strategies. Chronic liver diseases, including non-alcoholic fatty liver disease, cirrhosis, and hepatocellular carcinoma, often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operator-dependent imaging. This review explores the integration of AI across key domains such as big data analytics, deep learning-based image analysis, histopathological interpretation, biomarker discovery, and clinical prediction modeling. AI algorithms have demonstrated high accuracy in liver fibrosis staging, hepatocellular carcinoma detection, and non-alcoholic fatty liver disease risk stratification, while also enhancing survival prediction and treatment response assessment. For instance, convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis (F2-F4) and 0.89 for advanced fibrosis, with magnetic resonance imaging-based models reporting comparable performance. Advanced methodologies such as federated learning preserve patient privacy during cross-center model training, and explainable AI techniques promote transparency and clinician trust. Despite these advancements, clinical adoption remains limited by challenges including data heterogeneity, algorithmic bias, regulatory uncertainty, and lack of real-time integration into electronic health records. Looking forward, the convergence of multi-omics, imaging, and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care. Continued efforts in model standardization, ethical oversight, and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.