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Artificial intelligence in allergic airway diseases: A comprehensive review

Year: 2026

Type: article

Abstract: Artificial intelligence (AI) is rapidly reshaping the clinical management of allergic airway diseases, in which type 2 inflammation represents a major pathogenic axis across asthma, allergic rhinitis, and chronic rhinosinusitis (CRS). This Review summarizes AI-enabled advances across the clinical pathway in both lower-airway and upper-airway diseases, including asthma, allergic rhinitis, and CRS. In diagnostics, AI is accelerating a shift towards precision endotyping by integrating multi-omics data to identify molecular signatures of type 2 inflammation and by enabling quantitative interpretation of multimodal imaging for non-invasive assessment of airway structure. For monitoring, machine-learning approaches are increasingly translating routinely collected physiological signals—including acoustic biomarkers and exhaled-breath profiles—as well as real-world data from electronic health records into objective measures of disease activity and control. In prognostics, predictive models are being developed to forecast near-term exacerbations and to estimate long-term trajectories, including early-life determinants of life-course risk. AI is also emerging as an enabling layer for personalized management, supporting patient stratification for targeted biologics, predicting treatment response, and extending care through digital health ecosystems that facilitate self-management and remote follow-up. However, progress faces constraints from data quality issues, outcome heterogeneity, insufficient validation, and a persistent implementation gap between development and clinical use. We discuss how frontier directions—including causal inference, explainable AI, and patient-specific digital twins—could improve robustness, interpretability and clinical actionability. Taken together, an “augmented intelligence” paradigm, in which AI complements rather than replaces clinical expertise, has the potential to enable scalable, evidence-based personalization of care for type 2 inflammatory airway diseases.

Source: Allergy medicine.

DOI: 10.1016/j.allmed.2026.100130