ReviewCureus2026
Artificial Intelligence in Paediatric Allergy: From Diagnostic Support to Precision Medicine.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.