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Volume 14
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10.3390/jcm14175958
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Systematic Review

Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future: A Systematic Review

by
Ivan Taietti
1,2,†,
Martina Votto
1,2,†,
Marta Colaneri
3,4,
Matteo Passerini
3,4,
Jessica Leoni
5,
Gian Luigi Marseglia
1,2,
Amelia Licari
1,2 and
Riccardo Castagnoli
1,2,*
1
Pediatric Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
2
Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
3
Department of Infectious Diseases, ASST Fatebenefratelli Sacco University Hospital, 20157 Milan, Italy
4
Department of Pathophysiology and Transplantation, University of Milan, 20122 Milan, Italy
5
Department of Electronics and Information of Politecnico di Milano, 20133 Milan, Italy
*
Author to whom correspondence should be addressed.
†
These authors equally contributed to the work.
J. Clin. Med. 2025, 14(17), 5958; https://doi.org/10.3390/jcm14175958 (registering DOI)
Submission received: 4 July 2025 / Revised: 14 August 2025 / Accepted: 18 August 2025 / Published: 23 August 2025
(This article belongs to the Special Issue Inborn Errors of Immunity: Advances in Diagnosis and Treatment)
Versions Notes

Abstract

Background: Inborn errors of immunity (IEI) are mainly genetically driven disorders that affect immune function and present with highly heterogeneous clinical manifestations, ranging from severe combined immunodeficiency (SCID) to adult-onset immune dysregulatory diseases. This clinical heterogeneity, coupled with limited awareness and the absence of a universal diagnostic test, makes early and accurate diagnosis challenging. Although genetic testing methods such as whole-exome and genome sequencing have improved detection, they are often expensive, complex, and require functional validation. Recently, artificial intelligence (AI) tools have emerged as promising for enhancing diagnostic accuracy and clinical decision-making for IEI. Methods: We conducted a systematic review of four major databases (PubMed, Scopus, Web of Science, and Embase) to identify peer-reviewed English-published studies focusing on the application of AI techniques in the diagnosis and treatment of IEI across pediatric and adult populations. Twenty-three retrospective/prospective studies and clinical trials were included. Results: AI methodologies demonstrated high diagnostic accuracy, improved detection of pathogenic mutations, and enhanced prediction of clinical outcomes. AI tools effectively integrated and analyzed electronic health records (EHRs), clinical, immunological, and genetic data, thereby accelerating the diagnostic process and supporting personalized treatment strategies. Conclusions: AI technologies show significant promise in the early detection and management of IEI by reducing diagnostic delays and healthcare costs. While offering substantial benefits, limitations such as data bias and methodological inconsistencies among studies must be addressed to ensure broader clinical applicability.
Keywords: inborn errors of immunity (IEI); primary immunodeficiencies (PIDs); artificial intelligence (AI); machine learning (ML); genetics; early diagnosis inborn errors of immunity (IEI); primary immunodeficiencies (PIDs); artificial intelligence (AI); machine learning (ML); genetics; early diagnosis

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MDPI and ACS Style

Taietti, I.; Votto, M.; Colaneri, M.; Passerini, M.; Leoni, J.; Marseglia, G.L.; Licari, A.; Castagnoli, R. Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future: A Systematic Review. J. Clin. Med. 2025, 14, 5958. https://doi.org/10.3390/jcm14175958

AMA Style

Taietti I, Votto M, Colaneri M, Passerini M, Leoni J, Marseglia GL, Licari A, Castagnoli R. Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future: A Systematic Review. Journal of Clinical Medicine. 2025; 14(17):5958. https://doi.org/10.3390/jcm14175958

Chicago/Turabian Style

Taietti, Ivan, Martina Votto, Marta Colaneri, Matteo Passerini, Jessica Leoni, Gian Luigi Marseglia, Amelia Licari, and Riccardo Castagnoli. 2025. "Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future: A Systematic Review" Journal of Clinical Medicine 14, no. 17: 5958. https://doi.org/10.3390/jcm14175958

APA Style

Taietti, I., Votto, M., Colaneri, M., Passerini, M., Leoni, J., Marseglia, G. L., Licari, A., & Castagnoli, R. (2025). Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future: A Systematic Review. Journal of Clinical Medicine, 14(17), 5958. https://doi.org/10.3390/jcm14175958

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