The Role of Artificial Intelligence in Modern Allergology: A Review of Applications in Diagnosis, Prediction, and Management

ABSTRACT Artificial Intelligence is rapidly transforming allergology by enhancing diagnosis, risk prediction, automation, patient communication, education, and therapy development. Machine learning approaches, including convolutional neural networks, recurrent architectures, and transformer‐based models, enable analysis of complex datasets from genomics, imaging, clinical records, and patient‐reported outcomes. Artificial Intelligence applications in atopic disease diagnosis demonstrate high accuracy in imaging‐based detection, molecular phenotyping, and acoustic monitoring, while unsupervised learning methods such as Uniform Manifold Approximation and Projection and Hierarchical Density‐Based Spatial Clustering of Applications with Noise reveal distinct sensitisation clusters and patient risk profiles. Predictive modelling facilitates individualised management, including outcome prediction for oral food challenges, stratification of drug hypersensitivity risk, and forecasting disease progression in asthma and atopic dermatitis. Artificial Intelligence‐driven automation, such as skin prick test quantification and pollen monitoring, improves reproducibility and efficiency in clinical workflows. Additionally, Artificial Intelligence‐guided approaches are being explored in allergen immunotherapy development, including epitope mapping and hypoallergenic vaccine design, supported by experimental validation using assays such as enzyme‐linked immunosorbent assay and basophil activation tests. Large language models like ChatGPT show potential for patient engagement and education, though limitations in clinical reasoning and safety necessitate supervised use. Despite promising results, most Artificial Intelligence applications remain at early stages, with challenges including dataset size, generalisability, interpretability, cost, and integration into routine practice. Prospective validation, multicenter studies, and ethical oversight are essential for safe and effective implementation. Overall, Artificial Intelligence holds significant potential to advance allergology toward a predictive, personalised, and preventive model of care, offering new tools for precision diagnostics, risk assessment, and therapeutic innovation.