Global perspectives on artificial intelligence in allergology: Mapping validity, diversity, and context in allergy care

Allergology, as a branch of medicine focused on the identification, diagnosis, and management of allergic diseases, is rapidly evolving with the integration of Artificial Intelligence (AI) technologies. Worldwide, the application of AI ranges from screening and diagnosis to the personalization of management strategies, promising improved health outcomes amid a rising global allergy burden. However, substantial questions remain regarding the extent to which AI innovations developed in one context remain valid, effective, and equitable in different geographical, ethnic, genetic, environmental, and cultural settings (especially as allergy prevalence and triggers differ significantly across the globe). Recent studies have demonstrated remarkable diagnostic potential and prediction accuracy using machine learning models trained mainly on local datasets, yet global generalizability and inclusion of diverse population data are limited. This gap underscores an urgent need to critically assess how local realities, such as environmental exposures, genetic backgrounds, healthcare access, and sociocultural factors, modulate both the applicability and fairness of AI-driven solutions in allergology.This Research Topic aims to advance a comprehensive, globally informed understanding of AI in allergology by gathering insights from diverse regions and populations, prioritizing the contextual factors shaping AI solution validity and generalizability. We seek to foster an educational dialogue that addresses how regional characteristics (spanning geography, ethnicity, genetics, environment, culture, and even geopolitical determinants) influence AI-driven screening, diagnosis, and personalized allergy care. By welcoming contributions from researchers, clinicians, and experts across all regions through reviews, opinion pieces, and perspective papers, this collection aspires to map the current landscape, spotlight best practices, and identify knowledge and implementation gaps, while also considering ethical, economic, and practical ramifications for future innovation and policy.To gather further insights in the globally contextualized boundaries of AI use in allergology, we welcome articles that explore contributions and challenges across world regions, while carefully mapping and comparing local influences on algorithm development, evaluation, and deployment. While contributions from adjacent fields such as artificial intelligence and digital health are welcome where they bear directly on allergy care, this collection is anchored in the clinical and research context of allergology. We welcome submissions addressing, but not limited to, the following themes:o Comparative analyses of AI approaches in allergology between regions, countries, and populationso Impact of geography, ethnicity, genetics, and environment on AI model validity and generalizabilityo Role of cultural, social, and geopolitical factors in AI adoption, trust, and accessibility in allergy careo Clinician trust, adoption barriers, and implementation challenges in integrating AI into real-world allergy care settingso Expert perspectives on emerging best practices, ethical frameworks, and regional innovationo Strategies to ensure representative datasets and mitigate bias in AI tools for allergologyo Education and capacity-building for clinicians and health systems to adopt AI responsibly in diverse contextso Review and opinion pieces on the prospects, limitations, and research priorities for global AI deployment in allergy managementAll article types accepted by Frontiers are encouraged, with a particular emphasis on reviews, opinion, and perspective papers. Topic editors: Paolo Maria Matricardi, Polat Goktas, Robert Hamilton, Anthony Sunjaya, Joana Vitte, Yuan LUO Source: https://www.frontiersin.org/research-topics/75579/global-perspectives-on-artificial-intelligence-in-allergology-mapping-validity-diversity-and-context-in-allergy-care