From Imaging to Omics: AI-Driven Risk Stratification in Hepatology

AI-driven risk stratification in hepatology integrates imaging data with molecular and clinical information to identify patients at high risk of liver disease progression. Advanced imaging techniques such as shear-wave elastography, MRI proton density fat fraction, and multiphase CT—provide quantitative measures of liver stiffness, fat content, and tissue heterogeneity. When combined with omics data, including genomics, transcriptomics, and circulating proteomic or microRNA profiles, machine learning algorithms can detect early fibrosis and predict progression to cirrhosis with high accuracy. This multi-modal approach enables personalized risk scoring, guiding decisions on surveillance frequency, lifestyle interventions, and pharmacological therapy. By providing objective, reproducible, and individualized assessments, AI-driven risk stratification supports precision medicine and improves long-term outcomes in chronic liver disease.