Authors
Prav Hamal
Published in
Cureus. Volume 18. Issue 7. Pages e112707. Epub Jul 15, 2026.
Abstract
Paediatric allergic disease continues to increase globally, placing substantial pressure on healthcare systems and specialist allergy services. Current diagnostic approaches rely heavily on clinical history, skin prick testing, serum-specific immunoglobulin E (IgE), and oral food challenges, all of which have recognised limitations. Artificial intelligence (AI) has emerged as a promising tool capable of integrating complex data from structured electronic health records and unstructured notes to improve diagnostic accuracy, risk prediction, and healthcare delivery. This narrative review examined developments in AI applications within paediatric allergy between 2015 and 2025 and explored future opportunities and challenges for clinical implementation. A literature search of PubMed, MEDLINE, and Google Scholar was performed using combinations of the terms "paediatric allergy", "food allergy", "anaphylaxis", "artificial intelligence", "machine learning", "deep learning", and "clinical decision support", with original research articles, systematic reviews, and relevant commentaries included. After screening 280 articles, 16 were included in the final narrative synthesis. Three principal themes emerged: diagnostic support and risk prediction, clinical decision support systems, and digital health technologies. Machine learning models integrating clinical history, biomarker profiles, and component-resolved diagnostics demonstrated improved prediction of food allergy outcomes compared with traditional approaches alone. Emerging applications include risk stratification for severe allergic reactions, support for drug allergy de-labelling pathways, and AI-assisted interpretation of oral food challenge outcomes. Recent advances in generative AI and large language models have additionally created opportunities for patient education, clinical documentation, and service efficiency. However, most studies remain retrospective and lack robust external validation. Overall, AI has significant potential to transform paediatric allergy through improved diagnostic precision, personalised risk prediction, and enhanced healthcare delivery. This review summarises current applications of AI in diagnosis, clinical decision support, digital health and future precision allergy medicine while highlighting key challenges to implementation.
PMID:
42602687
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.
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