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

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.

Keywords: Hepatology; Artificial intelligence; Deep learning; Convolutional neural network; Natural language processing; Support vector machine; Graph neural network; Transformer model; Recurrent neural network

Core Tip: This minireview highlights artificial intelligence innovations in hepatology. It develops multi-modal data fusion strategies integrating imaging, genomics, lab results, and clinical records via transformer models and graph neural networks to uncover latent associations and build disease knowledge graphs. Convolutional neural networks enable automated, high-accuracy liver lesion detection and quantification in pathology/imaging. Artificial intelligence optimizes personalized antiviral therapy and liver transplant outcomes through drug-response prediction and graft-recipient matching. Natural language processing extracts critical insights from electronic health records. These approaches significantly advance diagnosis, treatment personalization, and translational research in precision hepatology.