Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine

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Abstract
Hepatology encompasses various aspects, such as metabolic-associated fatty liver disease, viral hepatitis, alcoholic liver disease, liver cirrhosis, liver failure, liver tumors, and liver transplantation. The global epidemiological situation of liver diseases is grave, posing a substantial threat to human health and quality of life. Characterized by high incidence and mortality rates, liver diseases have emerged as a prominent global public health concern. In recent years, the rapid advancement of artificial intelligence (AI), deep learning, and radiomics has transformed medical research and clinical practice, demonstrating considerable potential in hepatology. AI is capable of automatically detecting abnormal cells in liver tissue sections, enhancing the accuracy and efficiency of pathological diagnosis. Deep learning models are able to extract features from computed tomography and magnetic resonance imaging images to facilitate liver disease classification. Machine learning models are capable of integrating clinical data to forecast disease progression and treatment responses, thus supporting clinical decision-making for personalized medicine. Through the analysis of imaging data, laboratory results, and genomic information, AI can assist in diagnosis, forecast disease progression, and optimize treatment plans, thereby improving clinical outcomes for liver disease patients. This minireview intends to comprehensively summarize the state-of-the-art theories and applications of AI in hepatology, explore the opportunities and challenges it presents in clinical practice, basic research, and translational medicine, and propose future research directions to guide the advancement of hepatology and ultimately improve patient outcomes.

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