Artificial intelligence in pediatric allergic diseases: what the clinician needs to know

Artificial intelligence (AI) is reaching pediatric practice through consumer apps, wearables and decision-support tools, yet most children with allergic disease are managed by general pediatricians. This review summarizes what AI can and cannot do across pediatric allergic diseases, emphasising clinical readiness and the questions clinicians should ask before acting on an algorithmic output. Narrative review. MEDLINE/PubMed, Scopus and Google Scholar were searched for English-language publications (January 2015-June 2026) on machine learning, deep learning, digital biomarkers and large language models in pediatric asthma, wheeze, atopic dermatitis, food allergy and allergic rhinitis. Peer-reviewed studies were appraised for clinical applicability, validation status and implementation barriers. Machine learning trajectory analyses show that allergic disease follows multiple coexisting pathways rather than a single atopic march. Deep learning interprets atopic dermatitis images and respiratory sounds with accuracy approaching specialist assessment. Predictive models estimate oral food challenge outcomes and impending asthma exacerbations, and wearable-derived biomarkers may signal deterioration early. Readiness nevertheless varies widely: most applications remain at research or pilot stage, constrained by small single-center datasets, scarce external validation, under-representation of darker skin tones, and limited interoperability with clinical workflows.

AI is closer to the bedside in pediatric allergy than in many other areas of pediatrics, but few tools are ready for autonomous clinical use. No trial has yet shown that acting on an AI-derived risk estimate improves clinical outcomes in children. Pediatricians will increasingly encounter AI-derived outputs brought by families or embedded in health systems; appraising validation, representativeness and accountability is becoming a core clinical skill.

• Pediatric allergic diseases follow heterogeneous trajectories that conventional tools predict poorly. • AI models report high accuracy in image, audio, and risk-prediction tasks.

• Readiness, not reported accuracy, should govern clinical use: most tools remain research-stage. • Pediatricians need a practical framework to appraise AI outputs brought by families and health systems.