Applications of artificial intelligence in vasculitides: a systematic review
Open accessPublished version of a preprint
Abstract
OBJECTIVE: 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, Institute of Electrical and Electronics Engineers 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 the Prediction Model Risk of Bias Assessment Tool and Quality Assessment of Diagnostic Accuracy Studies-2. RESULTS: A total of 46 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 intravenous Ig resistance in Kawasaki disease, showed areas under the curves 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 large language models are needed to advance their clinical applicability across different vasculitis types.
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Isolated studies aim to leverage AI to classify inflammatory dermatoses or advance disease understanding with immunophenotyping, and newer AI models including deep learning and generative AI applications are underutilized.
“Isolated studies aim to leverage AI to classify inflammatory dermatoses or advance disease understanding with immunophenotyping. Newer AI models, including deep learning and generative AI applications, are underutilized (Omar et al, 2025).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Omar et al, 2025
The Discussion (page unavailable) says newer models such as large language models have not been explored in vasculitis, supporting a gap in generative-AI applications. But the Results report existing deep-learning studies, and the paper addresses vasculitis rather than classification of inflammatory dermatoses or immunophenotyping. It does not support those parts of the claim or establish that deep learning is underutilized. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAn AI model achieved high diagnostic performance for predicting Kawasaki disease and intravenous Ig resistance from health data.
“Among vasculitides, an AI model achieved high diagnostic performance for predicting Kawasaki disease and intravenous Ig resistance from health data (Omar et al, 2025)”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Omar et al, 2025
The Discussion (page unavailable) reports strong performance for models diagnosing Kawasaki disease from clinical and laboratory data and for other models predicting intravenous Ig resistance. It does not report that a single model performed both tasks, as the citing sentence implies. (LLM, conf=0.86)
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Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Rayyan-a web and mobile app for systematic reviews.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0384-4 · Cited by 26,092 · see citing works
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199 · see citing works
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · see citing works
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887 · see citing works
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Artificial intelligence in healthcare: transforming the practice of medicine
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0095 · Cited by 1,856 · see citing works
Multipotent mesenchymal stem cells from adult human synovial membrane
Bari, C. D., Dell’Accio, F., Tylżanowski, P., & Luyten, F. P. (2001) · Arthritis & Rheumatism · doi:10.1002/1529-0131(200108)44:8 · Cited by 1,815 · see citing works
Searching for and selecting studies
Lefebvre, C., Glanville, J., Briscoe, S., Littlewood, A., Marshall, C., Metzendorf, M., Noel‐Storr, A., Rader, T., Shokraneh, F., Thomas, J., et al. (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch4 · Cited by 647 · see citing works
PROSPERO: An International Register of Systematic Review Protocols
Schiavo, J. H. (2019) · Medical Reference Services Quarterly · doi:10.1080/02763869.2019.1588072 · Cited by 643 · see citing works
A Survey of Visual Transformers
Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J. S., Tian, J., Zhang, Y. S., Shi, Z., Jianping Fan, B., & He, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3227717 · Cited by 614 · see citing works
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 514 · see citing works
Self-citationA systematic review of natural language processing applied to radiology reports
Casey, A., Davidson, E., Poon, M., Dong, H., Duma, D., Grivas, A., Grover, C., Suárez-Paniagua, V., Tobin, R., Whiteley, W., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01533-7 · Cited by 234 · see citing works
Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review
Sorin, V., Barash, Y., Konen, E., & Klang, E. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.12.026 · Cited by 183 · see citing works
Self-citationApplied machine learning and artificial intelligence in rheumatology
Hügle, M., Omoumi, P., van Laar, J. M., Boedecker, J., & Hügle, T. (2020) · Rheumatology Advances in Practice · doi:10.1093/rap/rkaa005 · Cited by 178 · see citing works
Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios
Shreffler, J. R., & Huecker, M. R. (2020) · StatPearls · Cited by 166 · see citing works
Abdolmanafi, A., Duong, L., Dahdah, N., & Farida Cheriet, A. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.001203 · Cited by 130 · see citing works
What is new in computer vision and artificial intelligence in medical image analysis applications
Olveres, J., González, G., Torres-Robles, F., Moreno-Tagle, J. C., Carbajal-Degante, E., Valencia-Rodríguez, A., Méndez‐Sánchez, N., & Ramírez, B. E. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1151 · Cited by 124 · see citing works
Walkowiak, D., & Domaradzki, J. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-02023-9 · Cited by 84 · see citing works
Lam, J. Y., Shimizu, C., Tremoulet, A. H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00149-2 · Cited by 65 · see citing works
Omar, M., Brin, D., Glicksberg, B., & Klang, E. (2024) · American Journal of Infection Control · doi:10.1016/j.ajic.2024.03.016 · Cited by 59 · see citing works
Self-citationCurrent Concept and Epidemiology of Systemic Vasculitides
Katsuyama, T., Sada, K., & Makino, H. (2014) · Allergology International · doi:10.2332/allergolint.14-rai-0778 · Cited by 57 · see citing works
Ing, E. B., Miller, N. R., Nguyen, A. M., Su, W., Bursztyn, L. L. C. D., Poole, M., Kansal, V., Toren, A., Albreiki, D., Mouhanna, J. G., et al. (2019) · Clinical ophthalmology · doi:10.2147/opth.s193460 · Cited by 57 · see citing works
Doan, S., Maehara, C. K., Chaparro, J. D., Lu, S., Liu, R., Graham, A., Berry, E., Hsu, C., Kanegaye, J. T., Lloyd, D., et al. (2016) · Academic Emergency Medicine · doi:10.1111/acem.12925 · Cited by 48 · see citing works
Vasculitis syndrome—diagnosis and therapy
Okazaki, T., Shinagawa, S., & Mikage, H. (2017) · Journal of General and Family Medicine · doi:10.1002/jgf2.4 · Cited by 46 · see citing works
Nashwan, A. J., & Abujaber, A. A. (2023) · Cureus · doi:10.7759/cureus.42634 · Cited by 46 · see citing works
Tsai, C., Lin, C. R., Kuo, H., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.7489 · Cited by 44 · see citing works
Application of neural networks to the classification of giant cell arteritis
Astion, M. L., Wener, M. H., Thomas, R. G., et al. (1994) · Arthritis & Rheumatism · doi:10.1002/art.1780370522 · Cited by 40 · see citing works
Forrest, I. S., Petrazzini, B. O., Duffy, Á., Park, J. K., O’Neal, A. J., Jordan, D. M., Rocheleau, G., Nadkarni, G. N., Cho, J. H., Blazer, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37996-7 · Cited by 37 · see citing works
Self-citationLee, H., Eun, Y., Hwang, J. Y., & Eun, L. Y. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106970 · Cited by 35 · see citing works
Liu, J., Zhang, J., Huang, H., et al. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.756095 · Cited by 33 · see citing works
Novel Risk Assessment Tool for Immunoglobulin Resistance in Kawasaki Disease
Takeuchi, M., Inuzuka, R., Hayashi, T., et al. (2017) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000001621 · Cited by 32 · see citing works
Abdolmanafi, A., Chériet, F., Duong, L., et al. (2019) · Journal of Biophotonics · doi:10.1002/jbio.201900112 · Cited by 29 · see citing works
Artificial intelligence to analyze magnetic resonance imaging in rheumatology
Adams, L. C., Bressem, K. K., Ziegeler, K., et al. (2023) · Joint Bone Spine · doi:10.1016/j.jbspin.2023.105651 · Cited by 27 · see citing works
Sunaga, Y., Watanabe, A., Katsumata, N., et al. (2023) · Clinical Rheumatology · doi:10.1007/s10067-023-06502-1 · Cited by 26 · see citing works
Kuniyoshi, Y., Tokutake, H., Takahashi, N., Kamura, A., Yasuda, S., & Tashiro, M. (2020) · Frontiers in Pediatrics · doi:10.3389/fped.2020.570834 · Cited by 24 · see citing works
Ananda-Rajah, M. R., Bergmeir, C., Petitjean, F., Slavin, M. A., Thursky, K., & Webb, G. I. (2017) · JCO Clinical Cancer Informatics · doi:10.1200/cci.17.00011 · Cited by 23 · see citing works
Omar, M., Naffaa, M. E., Glicksberg, B. S., Reuveni, H., Nadkarni, G. N., & Klang, E. (2024) · Rheumatology Advances in Practice · doi:10.1093/rap/rkae120 · Cited by 23 · see citing works
Self-citationLinder, R., ORTH, I., HAGEN, E. C., van der Woude, F. J., & Schmitt, W. H. (2011) · The Journal of Rheumatology · doi:10.3899/jrheum.100814 · Cited by 22 · see citing works
Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning
Tang, Y., Liu, Y., Du, Z., et al. (2024) · BMC Pediatrics · doi:10.1186/s12887-024-04608-2 · Cited by 22 · see citing works
van Leeuwen, J. R., Penne, E. L., Rabelink, T. J., Knevel, R., & Teng, Y. K. O. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107757 · Cited by 20 · see citing works
Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering
Venerito, V., Emmi, G., Cantarini, L., Leccese, P., Fornaro, M., Fabiani, C., Lascaro, N., Coladonato, L., Mattioli, I., Righetti, G., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.860877 · Cited by 19 · see citing works
A machine learning model for distinguishing Kawasaki disease from sepsis
Li, C., Liu, Y., Zhang, D.-R., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-39745-8 · Cited by 19 · see citing works
González-Gay, M. Á., & Garcı́a-Porrúa, C. (2002) · Best Practice & Research Clinical Rheumatology · doi:10.1053/berh.2002.0260 · Cited by 17 · see citing works
Wang, Y., Cao, Y., Li, Y., Zhu, F., Yuan, M., Xu, J., Ma, X., & Li, J. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.112194 · Cited by 16 · see citing works
Azuma, J., Yamamoto, T., Nitta, M., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-68657-0 · Cited by 15 · see citing works
Güler, İ., & Übeylı, E. D. (2004) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2003.12.007 · Cited by 14 · see citing works
A deep convolutional neural network for Kawasaki disease diagnosis
Xu, E., Nemati, S., & Tremoulet, A. H. (2022) · Scientific Reports · doi:10.1038/s41598-022-15495-x · Cited by 14 · see citing works
Zhang, M., Ke, B., Zhuo, H., et al. (2022) · BMC Pediatrics · doi:10.1186/s12887-022-03557-y · Cited by 13 · see citing works
Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis
Dhirachaikulpanich, D., Xie, J., Chen, X., et al. (2024) · Ocular Immunology and Inflammation · doi:10.1080/09273948.2024.2305185 · Cited by 13 · see citing works
Nie, D., Zhan, Y., Xu, K., et al. (2023) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.14956 · Cited by 12 · see citing works
Lu, A., Li, K., Guo, S., Zhang, X., Su, G., & Yang, P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106271 · Cited by 12 · see citing works
Cao, T., Zhu, Y., & Zhu, Y. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/6991218 · Cited by 11 · see citing works
Joung, J., Oh, J. S., Yoon, J. M., et al. (2022) · BMC Pediatrics · doi:10.1186/s12887-022-03533-6 · Cited by 11 · see citing works
Hammam, N., Bakhiet, A., Latif, E. A. E., et al. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02130-6 · Cited by 10 · see citing works
Lu, Y., Zhang, Z.-L., Zhou, X., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108421 · Cited by 10 · see citing works
Ren, H., Lv, W., Shang, Z., et al. (2024) · BMC Medical Genomics · doi:10.1186/s12920-023-01702-9 · Cited by 9 · see citing works
Duan, Y., Wang, R., Huang, Z., Chen, H., Tang, M., Zhou, J., Hu, Z., Hu, W., Chen, Z., Qian, Q., et al. (2024) · Hellenic Journal of Cardiology · doi:10.1016/j.hjc.2024.08.003 · Cited by 9 · see citing works
Morris, A., Freitas, D. L. D., Lima, K. M. G., et al. (2022) · Molecules · doi:10.3390/molecules27072312 · Cited by 8 · see citing works
Quantitative Analysis of Retinal Vascular Leakage in Retinal Vasculitis Using Machine Learning
Keino, H., Wakitani, T., Sunayama, W., et al. (2022) · Applied Sciences · doi:10.3390/app122412751 · Cited by 8 · see citing works
Bertin, D., Bongrand, P., & Bardin, N. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063270 · Cited by 8 · see citing works
Deng, L., Zhao, J., Wang, T., et al. (2024) · Korean Journal of Pediatrics · doi:10.3345/cep.2024.00549 · Cited by 7 · see citing works
Prediction of renal damage in children with IgA vasculitis based on machine learning
Wang, J., Chu, H., & Pan, Y. (2022) · Medicine · doi:10.1097/md.0000000000031135 · Cited by 6 · see citing works
Ryyppö, R., Häyrynen, S., Joutsijoki, H., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107917 · Cited by 6 · see citing works
Predicting renal damage in children with IgA vasculitis by machine learning
Pan, M., Li, M., Li, N., et al. (2024) · Pediatric Nephrology · doi:10.1007/s00467-024-06432-3 · Cited by 6 · see citing works
Miyagi, Y., & Iwashima, S. (2024) · Clinical Drug Investigation · doi:10.1007/s40261-024-01373-z · Cited by 5 · see citing works
Luo, P., & Li, J. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04077-9 · Cited by 3 · see citing works
Coronary Artery Segmentation on Echocardiograms for Kawasaki Disease Diagnosis
Rodriguez, I. M., Mantecón, T., Femandez-Cooke, E., Grasa, C. D., Barrios, A., Toral, B., Albert, L., Rojo, P., & Cabrera, J. (2022) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb55594.2022.9991538 · Cited by 3 · see citing works
Amiot, V., Jiménez–del–Toro, O. A., Eyraud, P., et al. (2023) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms58004.2023.00301 · Cited by 3 · see citing works
Lee, K., Lee, H., Lee, M. H., et al. (2024) · doi:10.1109/aimhc59811.2024.00050 · Cited by 3 · see citing works
A Deep Learning Framework for Image-Based Screening of Kawasaki Disease
Lam, J. Y., Kanegaye, J. T., Xu, E., et al. (2023) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc40787.2023.10340801 · Cited by 2 · see citing works
Advancing Rheumatology Practice: Systematic Review of Natural Language Processing ApplicationsPreprint
Omar, M., Glicksberg, B. S., Reuveni, H., Nadkarni, G. N., & Klang, E. (2024) · medRxiv · doi:10.1101/2024.03.07.24303959 · Cited by 2 · see citing works
Self-citationVries, H. S., van Praagh, G. D., Nienhuis, P. H., Bouhali, O., Slart, R. H. J. A., & Alić, L. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00334 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [31]Siewert B, Błaszczyński J, Gowin E, et al. Can AI help pediatricians? Diagnosing Kawasaki disease using DRSA. Children (Basel) 2021; 8: 929. 34682194 PMC8534704
Cited by
Sorted by citations
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
- Authors
- Omar, M., Agbareia, R., Naffaa, M. E., Watad, A., Glicksberg, B. S., Nadkarni, G. N., & Klang, E.
- Affiliations
- Maccabi Healthcare Services (IL)
- Hadassah Medical Center (IL)
- Western Galilee Hospital (IL)
- Sheba Medical Center (IL)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Omar, Mahmud
- ORCID
- Venue
- ACR Open Rheumatology
- Publisher
- Wiley
- Published
- 2025-03
- Volume
- 7
- Issue
- 3
- Pages
- e70016-e70016
- ISSN
- 2578-5745 (ISSN-L)
- PMC
- PMC11911543
- OpenAlex
- W4408499704
- Cited by
- 11
- References
- 76
- Citations by year
- 2026: 9 · 2025: 2
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ACR Open Rheumatology (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Surgery › Kawasaki Disease and Coronary Complications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Vasculitis and related conditions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- vasculitis, artificial intelligence, Kawasaki disease, vasculitis diagnosis, outcome prediction, intravenous immunoglobulin resistance, deep learning, machine learning, large language models, diagnostic accuracy, inflammatory diseases
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