ObjectiveTo review the application of multimodal imaging techniques in autoimmune liver diseases (AILDs), focusing on disease diagnosis and the non-invasive assessment of inflammatory activity and liver fibrosis, and to define the value of non-invasive radiology and artificial intelligence (AI). The limitations and future directions of current research are also discussed. MethodsRelevant studies concerning multimodal imaging [ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI)/functional MRI] and AI in AILDs management were retrieved and reviewed. ResultsMultimodal imaging technologies provide critical evidence for clinical diagnosis and assessment of AILDs due to their non-invasive and dynamic advantages. Functional MRI enables precise quantification of liver fibrosis, inflammatory activity, and bile duct structural alterations, facilitating objective evaluation of disease progression. AI, with its powerful data mining and pattern recognition capabilities, offers new pathways to address challenges like subjectivity and lack of individualization in AILDs management. However, most studies are single-center with small samples, multimodal data integration remains insufficient, and the sensitivity of non-invasive imaging for early AILDs diagnosis is still limited. ConclusionsMultimodal imaging and AI can effectively address the limitations of traditional diagnostic methods, advancing the precision of AILDs diagnosis and treatment. Future efforts should focus on developing integrated analytical models that combine multidimensional data including imaging, clinical, and pathological information, to overcome early diagnosis bottlenecks and enhance the accuracy and practicality of treatment efficacy assessment and prognosis prediction.
