Alexandra F. Santos ^* Dominic Wong

• King's College London, London, United Kingdom

Food allergy represents an increasing global health issue, significantly impacting society on a personal and on a systems-wide level. The gold standard for diagnosing food allergy, the oral food challenge, is time-consuming, expensive, and carries risks of allergic reactions, with unpredictable severity. There is, therefore, an urgent need for more accurate, scalable, predictive diagnostic techniques.In this review, we discuss possible future directions in the world of food allergy diagnosis. We start by describing the current clinical approach to food allergy diagnosis, highlighting novel diagnostic methods recommended for use in clinical practice, such as the basophil activation test and molecular allergology, and go on to discuss tests that require more research before they can be applied to routine clinical use, including the mast cell activation test and bead-based epitope assay. Finally, we consider exploratory approaches, such as IgE glycosylation, IgG4, T and B cell assays, microbiome analysis, and plasma cytokines. Artificial intelligence is assessed for potential integrated interpretation of panels of diagnostic tests. Overall, a framework is proposed suggesting how combining established and emerging technologies can effectively enhance the accuracy of food allergy diagnosis in the future.