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

Table 1 Comparison of artificial intelligence models in hepatology

Model	Advantages	Disadvantages/limitations	Primary applications in hepatology
CNNs	Efficient feature extraction; rapid processing of large datasets; high accuracy and automation; integrates multimodal data	May require large labeled datasets; limited interpretability	Liver image analysis (magnetic resonance imaging/computed tomography/ultrasonography); lesion detection and classification; fibrosis staging; integration with clinical data for prognosis
Transformer	Powerful sequence modeling; multi-task processing; excels at global feature extraction and multiscale analysis	High computational complexity; limited adaptability to small datasets	Multi-modal data fusion (imaging + genomics + clinical records); prediction of survival outcomes; gene-imaging association analysis
GNNs	Models complex graph structures; dynamically updates knowledge graphs; enables reasoning across multi-source data	Requires well-structured graph data; computational cost may be high for large networks	Construction of disease knowledge graphs; identification of therapeutic targets; analysis of gene-protein-metabolite interactions
Radiomics	Extracts high-dimensional features from images; non-invasive and efficient; improves diagnostic and prognostic performance	Dependent on image quality and standardization; requires validation in multi-center settings	Diagnosis and classification of liver cancer; cirrhosis staging; prediction of survival outcomes
NLP	Extracts valuable information from unstructured text; integrates multi-source medical information	Limited by data quality and heterogeneity; may require domain-specific tuning	Analysis of electronic health records; construction of knowledge graphs for clinical decision support
Multi-modal fusion	Enhances predictive performance by integrating imaging, genomic, lab, and clinical data	Complexity in aligning and fusing heterogeneous data sources	Precise diagnosis; severity assessment; personalized treatment planning; comprehensive predictive modeling

CNN: Convolutional neural networks; GNN: Graph neural networks; NLP: Natural language processing.