The fast progress of synthetic intelligence (AI) in healthcare is promising for screening and early analysis in settings that closely depend on skilled experience, similar to uncommon illness (RDs) like Inborn Errors of Immunity (IEI). Nonetheless, the event of AI algorithms for IEI and different RDs faces essential challenges similar to knowledge set sizes, availability and harmonization. Equally, the implementation of AI-based methods for screening and analysis of IEI in real-world eventualities is hampered by a number of elements together with stakeholders’ acceptance, moral and authorized constraints, and technological obstacles.
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