Pollen-related allergic diseases, including allergic rhinoconjunctivitis and asthma, affect a growing proportion of the population and have substantial consequences for quality of life and healthcare systems. Conventional pollen forecasts, which rely on fixed pollen traps and meteorological data, are limited in spatial granularity, real-time responsiveness, and individual relevance. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a paradigm shift in the modelling of pollen release, forecast exposure, and alert allergic patients. This article provides a comprehensive overview of ML-based pollen forecasting systems, clarifying their underlying principles in accessible terms for clinicians and presenting practical and published tools that allergologists can integrate into routine practice. By combining aerobiological data, meteorological models, and patient-reported outcomes, ML enables more personalized, precise, and timely allergy management. We review the fundamental mechanisms of pollen release and dispersion and illustrate how ML models can improve predictive accuracy. Key platforms are compared in terms of clinical usability. We present real-world use cases showing how ML-driven alerts can help optimize treatment plans and support patient education. Practical insights are provided on the evaluation, implementation, and limitations of these tools. ML is not a distant technology—it is already transforming pollen forecasting and allergy alerts. This article aims to equip allergologists with the knowledge needed to evaluate and adopt these tools, enabling a more proactive and personalized approach to managing pollen allergies.
Corriger et al. (Tue,) studied this question.