Current perspectives and challenges of using AI in immunodeficiencies.

Abstract

The rapid growth of artificial intelligence (AI) in healthcare is promising for screening and early diagnosis in settings that heavily rely on professional expertise, such as rare disease (RDs) like Inborn Errors of Immunity (IEI). However, the development of AI algorithms for IEI and other RDs faces important challenges such as data set 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 technological barriers. Consequently, while the body of literature on AI-based solutions for early diagnosis of IEI continues to expand, clinical utility and widespread implementation remains 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.