Childhood allergic diseases, including asthma, rhinitis and eczema, are prevalent conditions that share strong genetic and environmental components. Diagnosis relies on clinical history and measurements of allergen-specific IgE. We hypothesize that a multi-omics model could accurately diagnose childhood allergic disease. We show that nasal DNA methylation has the strongest predictive power to diagnose childhood allergy, surpassing blood DNA methylation, genetic risk scores, and environmental factors. DNA methylation at only three nasal CpG sites classifies allergic disease in Dutch children aged 16 years well, with an area under the curve (AUC) of 0.86. This is replicated in Puerto Rican children aged 9–20 years (AUC 0.82). DNA methylation at these CpGs additionally detects allergic multimorbidity and symptomatic IgE sensitization. Using nasal single-cell RNA-sequencing data, these three CpGs associate with influx of T cells and macrophages that contribute to allergic inflammation. Our study suggests the potential of methylation-based allergy diagnosis.

The field of medicine is witnessing an exponential growth of interest in Artificial Intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy and immunology. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

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