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

Abstract

Autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC) require clinicians to combine heterogeneous clinical, biochemical, histological, and imaging data. This minireview examined disease-specific artificial intelligence studies that focused on validation, comparators, and clinical use. Multicenter models distinguished drug-induced liver injury from AIH and AIH from PBC, although performance falls in external cohorts and treatment response prediction in AIH was largely absent. PBC models identified phenotypes and predicted the ursodeoxycholic acid response; however, none has shown benefit beyond the GLOBE and UK-PBC pathways. In PSC, primary sclerosing cholangitis risk estimate tool and quantitative magnetic resonance cholangiopancreatography had the strongest comparative evidence; computed tomography, proteomics, and cholangiocarcinoma-risk models were still retrospective. Most studies used small samples, single-center data, imbalanced classes, incomplete calibration, and few prospective validations. Current tools should remain adjunct tools. Before using a model, clinicians should check its intended use, comparators, external validation, calibration, decision curve benefit, and subgroup performance. Multinational prospective validation, transparent reporting, workflow testing, and post-deployment monitoring are required.

Keywords: Artificial intelligence; Machine learning; Deep learning; Autoimmune hepatitis; Primary biliary cholangitis; Primary sclerosing cholangitis; Autoimmune liver disease; Precision medicine; Digital pathology; Large language models

Core Tip: Artificial intelligence can be used to quantify images and combine multimodal data in autoimmune liver disease; however, most models remain investigational. The best-supported evidence currently comes from externally validated differential diagnosis models for autoimmune hepatitis, established machine learning prognostic tools for primary sclerosing cholangitis, and treatment response models for primary biliary cholangitis. However, these tools require local validation and monitoring during clinical testing. Clinicians should seek calibration, comparison with current care, decision curve benefit, and subgroup performance; however, the area under the curve alone is insufficient. No current model supports autonomous diagnosis or changes in therapy or surveillance without guideline-based clinical judgment.