Bronchial asthma is identified in the review as a prevalent chronic inflammatory airway disease with a growing global burden. The authors highlight that one of the major shortcomings of current care is underdiagnosis, which limits timely initiation of appropriate therapy. The systematic review synthesizes published research on the role of artificial intelligence in improving diagnostic processes for asthma across clinical settings. The PubMed abstract emphasizes that AI technologies have shown unique advantages for disease identification, although specific algorithmic approaches, diagnostic performance metrics, and comparative data were not reported in the abstract and require consultation of the full text for details.
Assessment and ongoing monitoring are essential components of asthma management, yet the review notes existing limitations in accurate prognosis evaluation and risk prediction. According to the article abstract, AI methods have been applied to assessment tasks with the potential to enhance precision in evaluating disease control and predicting risk. The review covers research that explores these applications across the disease course, though the abstract does not enumerate particular monitoring metrics, model outputs, or validation results. Clinicians and researchers interested in methodologic specifics should refer to the full-text review for reported study designs and evidence syntheses.
The review identifies limited individualization of treatment strategies as a key challenge in contemporary asthma care. It positions artificial intelligence as a promising tool to support greater personalization of therapy, potentially tailoring interventions to individual patient characteristics and disease trajectories. The abstract frames AI as demonstrating distinctive advantages in this domain but does not list concrete clinical decision-support systems, therapeutic algorithms, or trial outcomes in the abstract itself. The article aims to summarize available research to inform further development and application of AI-based personalized treatment approaches.
Long-term management of bronchial asthma requires sustained patient engagement and accurate prognostic insight. The review points to ongoing issues with poor long-term patient adherence and suboptimal precision in prognosis evaluation and risk prediction. It reports that AI technologies have shown benefits in follow-up and longitudinal management, including areas related to adherence support and outcome prediction. The abstract does not specify the types of digital interventions, remote monitoring tools, or predictive models examined; readers should consult the full article for comprehensive descriptions of follow-up interventions, adherence-improving strategies, and prognostic model findings included in the systematic review.
The abstract frames the systematic review as offering new perspectives for continued research and application of AI in whole-course asthma management. It acknowledges persistent challenges in diagnosis, individualized treatment, adherence, and prognosis where AI may contribute but indicates that further research is needed. The PubMed entry does not detail specific limitations of the included studies, heterogeneity of evidence, or recommendations on implementation pathways in clinical practice; these details are likely in the full text. Thus, the abstract serves as a high-level synthesis and a call for more detailed empirical and translational work to realize AI's potential in asthma care.
This systematic review was published in Zhonghua Jie He He Hu Xi Za Zhi (2026 Aug 12;49(8):891-896) with PMID 42557078 and DOI 10.3760/cma.j.cn112147-20260316-00149. The article is in Chinese and includes an English abstract. Authors are affiliated with the University of Shanghai for Science and Technology and the Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine. The authors declared no conflicts of interest. Funding sources listed in the PubMed entry include a National Science and Technology Major Project (2026ZD0556002) and the National Natural Science Foundation of China (82270027).
Note: The PubMed abstract summarizes the scope and conclusions of the systematic review but does not provide methodological specifics, algorithmic details, or quantitative performance metrics in the abstract view. For study-level data, model descriptions, validation results, and implementation recommendations, consult the full text available via the journal or the provided DOI link.