Published Paper Details:

AI has been and still is used in many allergic reactions and studies, mainly to characterize disease endotypes and phenotypes and create models to forecast future disease outcomes. But the majority of implementations have relied on comparatively basic data from a single source, like surveys. Furthermore, reporting and methodological approaches are deficient. This review offers a useful, practical manual for carrying out AI-based research in allergies reactions. It includes an overview of common pitfalls and solutions, a blueprint for organizing analysis pipelines (from variable selection to result interpretation), and an introduction to fundamental AI concepts and techniques.AI has the potential to improve allergy surveillance by integrating environmental and population-level data, power digital twins, and facilitate augmented reality training.AI has been applied to the management, diagnosis, treatment, and prediction of allergic diseases in recent years. People's mental health and quality of life can be severely impacted by allergic diseases, which are a type of chronic non-communicable disease that can affect many different systems and organs. This paper examines the use of AI in asthma, atopic dermatitis, food allergies, allergic rhinitis, and urticaria from the standpoints of disease prediction, diagnosis, treatment, and management.