The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI’s applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, remains a leading cause of cancer-related mortality worldwide. Its often-silent progression results in late-stage diagnosis, limiting curative options and necessitating systemic therapy for many patients. The presence of underlying cirrhosis in most cases further complicates treatment decisions. While the approval of sorafenib in 2007 marked a major milestone in systemic therapy for HCC, the treatment landscape has since evolved significantly, particularly with the advent of immune checkpoint inhibitors and anti-angiogenic agents. Combination regimens, such as atezolizumab plus bevacizumab, have demonstrated superior outcomes and are now considered standard first-line options. Despite these advances, efforts to translate insights from HCC’s molecular pathogenesis into personalized treatments have been limited. This narrative review explores the current systemic therapy options for HCC, from first-line to subsequent-line treatments, and highlights emerging strategies, including novel immunotherapies and targeted agents. We emphasize the need for individualized treatment approaches that consider tumor biology, liver function, and performance status, and we outline future directions for research aimed at improving outcomes in this complex and evolving field.

The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI, particularly machine learning (ML) and deep learning (DL), harnesses vast datasets and complex algorithms to enhance clinical decision-making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, such as fibrosis and cirrhosis, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.

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