Artificial intelligence in autoimmune liver diseases: Diagnosis, risk stratification, and personalized care

Figure 2 A multimodal, predict-personalize-monitor framework for artificial intelligence in autoimmune liver disease. Multimodal patient data (clinical and biochemical variables, autoantibody patterns, digitized whole-slide histology, cross-sectional and magnetic resonance cholangiographic imaging, and multi-omics) are integrated by machine learning and deep learning methods, including gradient boosting, convolutional neural networks, radiomics, natural language processing and large language models, and emerging multimodal foundation models. The resulting clinical applications are grouped as prediction (diagnosis and differential diagnosis, disease sub-phenotype, fibrosis stage, and outcome and cancer risk), personalization (choice and intensity of therapy, immunosuppression duration in autoimmune hepatitis, second-line therapy timing in primary biliary cholangitis, and surveillance intensity in primary sclerosing cholangitis), and monitoring (dynamic risk updating, treatment-response tracking and deviation alerts). The feedback loop denotes continuous monitoring and model updating. AI: Artificial intelligence; CT: Computed tomography; MRCP: Magnetic resonance cholangiopancreatography; MRI: Magnetic resonance imaging.