The Integration of Multi-omics With Artificial Intelligence in Hepatology: A Comprehensive Review of Personalized Medicine, Biomarker Identification, and Drug Discovery

The evolution of high-throughput technologies has expanded the role of multi-omics in hepatology, moving away from traditional hypothesis-driven research toward integrative, data-driven models. However, high cost and resource intensity have limited widespread adoption. The multi-omics datasets for liver diseases are still relatively small. Progress has been made in integrating a few omics types, particularly in combining genomics with transcriptomics, proteomics, or metabolomics for liver disease research. However, fully integrated multi-omics studies remain limited, with most research focusing on two or three omics layers rather than comprehensive multi-modal integration. Emerging approaches such as federated learning can be leveraged to securely integrate multi-omics data, advance AI-driven biomarker discovery, and enhance precision medicine strategies across institutions.