Big Data and Artificial Intelligence: Current State and Future Opportunities in Allergy and Immunology

Artificial intelligence (AI) and big data are reshaping the field of allergy and immunology, offering new opportunities to improve patient care, accelerate research, and inform clinical decision-making. The increasing availability of diverse data sources, including electronic health records, wearable devices, multi-omic profiles, environmental sensors, and patient-reported outcomes, has created an environment ready for innovation. Artificial intelligence techniques, particularly machine learning, are being applied to identify complex disease phenotypes, predict exacerbations, personalize treatment strategies, automate diagnostic tests interpretation, and streamline clinical documentation. Real-world examples already demonstrate the potential of AI and big data to support earlier diagnosis, optimize selection of biologics, and generate real-world evidence on treatment effectiveness and safety. However, several challenges remain, including the need for standardized data integration, protection of patient privacy, avoidance of algorithmic bias, and development of explainable, trustworthy AI systems. Ethical and practical considerations, such as equity in model development, transparency, and workflow integration, are critical for successful and responsible adoption in clinical practice. Lessons from other specialties, such as radiology and oncology, provide valuable models for implementation and highlight the importance of multidisciplinary collaboration. As the field moves forward, deliberate investment in technical infrastructure, governance, and clinician training will be essential to realize the promise of these technologies. In this context, this review provides an overview of these developments and highlights key considerations for their integration into clinical practice.