2026-04-13T01:18:49Zhttps://recercat.cat/oai/requestoai:recercat.cat:2072/4868072025-09-17T08:40:44Zcom_2072_98col_2072_378192
2025-09-17T08:40:43Z
urn:hdl:2072/486807
Current perspectives and challenges of using artificial intelligence in immunodeficiencies
Rivière, Jacques G.
Cantenys Sabà, Roser
Carot-Sans, Gerard
Piera-Jiménez, Jordi
Butte, Manish J.
Soler-Palacín, Pere
Peng, Xiao P.
Universitat Autònoma de Barcelona
Artificial intelligence
Machine learning
Inborn errors of immunity
Primary immunodeficiency
Secondary immunodeficiency
Predictive modeling
Genomics
Electronic health records
Clinical decision support
Implementation science
Screening algorithm
Altres ajuts: acords transformatius de la UAB
The rapid growth of artificial intelligence (AI) in health care is promising for screening and early diagnosis in settings that heavily rely on professional expertise, such as rare diseases like inborn errors of immunity (IEI). However, the development of AI algorithms for IEI and other rare diseases faces important challenges such as dataset sizes, availability and harmonization. Similarly, the implementation of AI-based strategies for screening and diagnosis of IEI in real-world scenarios is hampered by multiple factors including stakeholders' acceptance, ethical and legal constraints, and technologic barriers. Consequently, while the body of literature on AI-based solutions for early diagnosis of IEI continues to expand, clinical utility and widespread implementation remain limited. In this review, we provide an up-to-date comprehensive review of current applications and challenges facing AI use for IEI diagnosis and care.
2025-09-17T08:40:43Z
2025-09-17T08:40:43Z
2025
Article de revisió
http://hdl.handle.net/2072/486807
The journal of allergy and clinical immunology ; 2025
open access
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