ReviewEuropean respiratory review : an official journal of the European Respiratory Society2026
Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.
Review in European respiratory review : an official journal of the European Respiratory Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe asthma remains a challenging, heterogeneous condition, despite significant advances in therapeutic strategies. A subset of patients continues to experience poor control, frequent exacerbations and a high burden of disease. 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.