Applications of Artificial Intelligence in Vasculitides: A Systematic Review

Abstract

Background and Aim: Vasculitides are rare inflammatory disorders that sometimes can be difficult to diagnose due to their diverse presentations. This review examines the use of Artificial Intelligence (AI) to improve diagnosis and outcome prediction in vasculitis. Methods: A systematic search of PubMed, Embase, Web of Science, IEEE Xplore, and Scopus identified relevant studies from 2000 to 2024. AI applications were categorized by data type (clinical, imaging, textual) and by task (diagnosis or prediction). Studies were assessed for risk of bias using PROBAST and QUADAS-2 tools. Results: Forty-six studies were included. AI models achieved high diagnostic performance in Kawasaki Disease, with sensitivities up to 92.5% and specificities up to 97.3%. Predictive models for complications, such as IVIG resistance in Kawasaki Disease, showed AUCs between 0.716 and 0.834. Other vasculitis types, especially those using imaging data, were less studied and often limited by small datasets. Conclusion: The current literature shows that AI algorithms can enhance vasculitis diagnosis and prediction, with deep and machine learning models showing promise in Kawasaki Disease. However, broader datasets, more external validation, and the integration of newer models like LLMs are needed to advance their clinical applicability across different vasculitis types. Keywords: Vasculitis, Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing.