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Early diagnosis, effective treatment selection, and accurate prognostic stratification remain critical unmet needs in clinical hepatology. Artificial intelligence, particularly machine learning and deep learning, has emerged as a promising approach to addressing these challenges through the integration and analysis of large-scale, heterogeneous biomedical datasets. This narrative review synthesizes advances in artificial intelligence applications in hepatitis research and clinical practice published over the past decade, organized across three clinical domains: diagnosis, treatment optimization, and prognosis prediction. In the diagnostic domain, artificial intelligence\u2013based approaches have been applied to medical imaging, digital pathology, and multi-omics data to enhance the detection, classification, and staging of liver disease. 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