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The advent of multi-omic technologies, including genomics, transcriptomics, proteomics and metabolomics, has opened new avenues for understanding the molecular underpinnings of asthma. When combined with artificial intelligence (AI), these approaches hold the potential to transform and augment the management of severe asthma by identifying key biomarkers, refining disease endotypes and enabling personalised treatment strategies. This review explores the role of AI-integrated multi-omic approaches in asthma research, highlighting how AI-driven models can analyse vast datasets to uncover patterns often missed by traditional methods. These insights can improve diagnostic precision, predict therapeutic responses and guide the development of novel, targeted therapies. Key areas of focus include genetic loci associated with asthma severity, single-cell RNA sequencing to uncover cellular heterogeneity, and proteomic profiles that differentiate asthma phenotypes. Through this review, we aim to provide readers with a clear understanding of the current landscape in severe asthma research, highlighting the breakthroughs achieved through AI-integrated multi-omic approaches. Additionally, we aspire to guide the future direction of the field by addressing the challenges that remain in translating these discoveries into clinical practice. By fostering a deeper understanding of the potential of these technologies, we hope to inspire further innovations that will pave the way for precision medicine to transform severe asthma management, ultimately leading to more personalised and effective treatment options for patients.<\/jats:p>","DOI":"10.1183\/16000617.0040-2025","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T08:30:27Z","timestamp":1779870627000},"page":"250040","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches"],"prefix":"10.1183","volume":"35","author":[{"given":"Sundhas Rafeeq","family":"Valappil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saba","family":"Al Heialy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"2026062507402373000_35.180.250040.1","first-page":"1","article-title":"The Global Asthma Report 2022","volume":"26","year":"2022","journal-title":"Int J Tuberc Lung Dis"},{"key":"2026062507402373000_35.180.250040.2","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s40265-016-0583-2","article-title":"Reslizumab: first global approval","volume":"76","author":"Markham","year":"2016","journal-title":"Drugs"},{"key":"2026062507402373000_35.180.250040.3","first-page":"89","article-title":"FDA approves first-in-class TSLP-targeted antibody for severe asthma","volume":"21","author":"Mullard","year":"2022","journal-title":"Nat Rev Drug Discov"},{"key":"2026062507402373000_35.180.250040.4","unstructured":"Kumar C , Zito PM . 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