Machine learning in allergy research: A bibliometric review

The emergence of big data and analytic approaches initiated research efforts to characterise different subtypes of allergic diseases, including tracking disease progression and identifying patterns that may offer insight into their development and progression. Triangulation from different data sources and study types may help to elucidate the directionality of relationships between variables at a very individual level by modelling the complex interdependencies between multiple dimensions (e.g., genome, transcriptome, epigenome, microbiome, and metabolome), thereby moving away from associative to a more causal analysis. To ascertain the role of machine learning in allergy research, we conducted a comprehensive systematic review of the current literature. The findings highlight and underscore the potential of using AI/ML approaches in advancing our understanding of allergic diseases, which ultimately enhances patient care through improved prevention, diagnosis, and management strategies. It is important to emphasise that there is no single 'best' analytical method, highlighting the importance of cross-disciplinary collaborations. A team science approach is crucial for ensuring the application of appropriate methodologies tailored to the research question at hand and that context-specific interpretations are being made, supported by critical appraisal from both the front- (e.g., clinicians) and back-end (e.g., analysts) of research processes.