Future Perspectives for Artificial Intelligence in Allergy: Advances in Diagnosis, Monitoring, and Personalized Care
Clinical Snapshot
PICO Framework
| P — Population | Patients with allergic diseases including asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions |
| I — Intervention | Artificial intelligence techniques (expert systems, machine learning, deep learning, natural language processing) |
| C — Comparator | Traditional diagnostic and monitoring approaches in allergy care |
| O — Outcomes | Diagnostic efficiency, therapeutic precision, patient outcomes, risk prediction accuracy, disease monitoring capabilities |
Bottom Line
This narrative review provides a comprehensive overview of artificial intelligence applications in allergy care, covering diagnosis, monitoring, and personalized treatment across major allergic conditions. While the review highlights promising advances in expert systems, machine learning, and deep learning applications, it appropriately acknowledges significant implementation challenges including model interpretability, validation requirements, and regulatory barriers. The multidisciplinary authorship strengthens credibility, but the narrative methodology limits systematic evaluation of evidence quality. For Australian clinicians, this review offers valuable insights into emerging AI technologies that may transform allergy practice, though practical implementation will require careful consideration of TGA regulatory requirements and integration with existing clinical workflows. The review serves as a useful foundation for understanding AI potential in allergy care while maintaining appropriate caution about current limitations.
Key Findings
P Value: Not applicable for narrative review
Effect Size: Not quantified - narrative synthesis
Primary Outcome: AI demonstrates potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes across multiple allergic diseases
Nnt Or Sensitivity: Specific performance metrics not provided in abstract
Confidence Interval: Not applicable for narrative review
Clinical Application
Implementation challenges include model interpretability, external validation requirements, and regulatory approval processes Relevant to Australian allergy practice but would require TGA approval for diagnostic AI tools and integration with existing clinical pathways. RACGP guidelines would need updating to incorporate AI-assisted decision making. Patients with asthma, anaphylaxis, atopic dermatitis, food allergies, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions
Abstract
Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.
References
- 1.Moreno, E., Moreno, V., Curto, B., & Dávila, I. (2026). Future perspectives for artificial intelligence in allergy: Advances in diagnosis, monitoring, and personalized care. Journal of Investigational Allergology & Clinical Immunology. https://doi.org/10.18176/jiaci.1154
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