AI is emerging as a promising tool in the diagnosis and management of vasculitides, a group of rare inflammatory disorders that pose significant diagnostic challenges. A new systematic review analyzed AI applications in vasculitis care, revealing potential benefits in early detection and outcome prediction, particularly for Kawasaki disease.
A systematic search of PubMed, Embase, Web of Science, Institute of Electrical and Electronics Engineers Xplore, and Scopus identified 46 relevant studies published between 2000 and 2024. These studies assessed AI applications using clinical, imaging, and textual data for either diagnosis or outcome prediction. The review also evaluated the risk of bias using established assessment tools.
AI models demonstrated high diagnostic accuracy in Kawasaki disease, a medium-vessel vasculitis. Sensitivities reached up to 92.5%, while specificities were as high as 97.3%. These results suggest AI could be an effective tool for early detection, potentially improving treatment outcomes.
AI has also shown promise in predicting complications, such as intravenous immunoglobulin (IVIG) resistance in Kawasaki disease. Predictive models reported areas under the curve ranging from 0.716 to 0.834, indicating moderate to strong performance in identifying patients at risk of treatment resistance.
The reviewed studies applied various AI techniques, including:
Machine learning (ML): Traditional models like logistic regression and Random Forests were used for predictive analysis.
Deep learning (DL): Convolutional neural networks (CNNs) were employed for imaging-based diagnosis, particularly in vascular assessment.
Natural language processing (NLP) and large language models (LLMs): These models analyzed clinical notes to identify key diagnostic markers.
While AI has shown significant promise in Kawasaki disease, its application to other vasculitis types remains limited. Studies using imaging data were often constrained by small sample sizes, affecting the generalizability of findings. The review emphasizes the need for broader datasets, more extensive external validation, and the integration of newer AI models to improve clinical applicability.
AI is proving to be a valuable tool in vasculitis diagnosis and prediction, particularly in Kawasaki disease. However, expanding its use across other vasculitis types will require further research, larger datasets, and improved validation methods. As AI continues to evolve, it may become a key asset in vasculitis management, enhancing diagnostic accuracy and patient care.
Reference: Omar M et al. Applications of Artificial Intelligence in Vasculitides: A Systematic Review. ACR Open Rheumatol. 2025. doi:10.1002/acr2.70016.
Anaya Malik | AMJ
