Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].

BACKGROUND: While artificial intelligence (AI) offers unprecedented capabilities for predictive modeling and precision asthma management, there is an urgent clinical necessity to successfully translate these rapid algorithmic innovations into real-world respiratory care. The exponential growth of cross-disciplinary AI literature has paradoxically created information overload for clinicians, obscuring underlying translational friction and hindering evidence-based implementation. Consequently, bibliometric analysis serves as the optimal quantitative vehicle to decode this vast scientific architecture. This study aims to objectively map the evolutionary trajectory, global research landscape, and emerging hotspots of AI in asthma, providing actionable roadmaps to reconcile computational development with clinical practice. METHODS: A comprehensive literature search was conducted utilizing the Web of Science Core Collection database for studies related to AI in asthma, with the retrieval timeframe updated from January 1, 2016, to June 4, 2026. Following strict inclusion and exclusion criteria (restricted to English-language original articles and peer-reviewed reviews), a finalized dataset of 1,967 publications was extracted. Raw metadata parameters, including citations and bibliographic information, were exported for network topology analysis. Data synthesis was executed using specific algorithmic parameters in CiteSpace (for structural centrality and citation burst detection), VOSviewer (for co-authorship and keyword clustering), and the Bibliometrix R-package (for thematic evolution mapping). RESULTS: The field has experienced robust exponential growth (22.24% per annum), with a pivotal inflection point in 2019. Geographically, a dual-centric geopolitical landscape exists between the United States and China in publication volume; yet, the United Kingdom serves as the ultimate global hub with superior network centrality, alongside highly integrated networks from nations like Australia and France. Institutional analysis highlights a productive yet fragmented landscape driven by prolific powerhouses such as Harvard Medical School and Imperial College London, while high-centrality nodes like the University of Zurich and Johns Hopkins University cross-link clinical and algorithmic clusters. Thematically, the field has undergone a distinct three-epoch technological trajectory: from early statistical clustering [2016-2019] to machine learning-based electronic health record mining [2020-2023], and recently to advanced deep learning architectures and multi-modal integration [2024-2026]. Current research frontiers focus on multi-omics precision phenotyping, real-time exacerbation prediction via wearables, and causal inference for personalized therapy. CONCLUSIONS: While AI paradigms in asthma research have rapidly advanced, current literature is fundamentally constrained by profound translational friction stemming from an over-reliance on retrospective datasets, which introduces critical structural biases and limits clinical generalizability. To effectively translate algorithms into clinical utility, future research must urgently deploy federated learning frameworks to securely overcome global data silos, and prioritize prospective, multicenter pragmatic trials to validate AI-driven predictive interventions in real-world respiratory care.