## 1 Introduction
### Artificial Intelligence in Asthma Management  
Diverse ML- and DL-oriented applications and methods have been introduced for asthma management as depicted in Figure 1. AI can assist in monitoring asthma through weather and environmental monitoring for safe route recommendations, predicting event triggers using peak expiratory flow rate analysis, predicting relevant symptoms, and analyzing chest imaging data, including X-rays and CT scans. The highlighted techniques extensively support real-time monitoring and tailored treatment strategies, precisely with the help of AI-emerged wearable devices [18]. Asthma is exceptionally rich in providing beneficial data with respect to algorithms training; such data could have various forms, including biomedical signals detected from the human skin surface, cough sound recordings through devices, electronic health records (EHRs) from the hospitals, and weather and environmental factors measured through the sensors. A patient can record their data by themselves multiple times daily to enrich the dataset using a mechanical peak expiratory flow device, wearable devices, and cough sound recordings. Enormous training data can produce a robust AI model for the diagnosis. A study by [PERSON] et al. [3] highlighted several ML models effective in asthma management using EHR data, including CatBoost, which handles categorical features, logistic regression (LR), which used probability prediction, a support vector machine (SVM) that locates the optimal hyperplane, and naive Bayes (NB) which assumes feature independence. A parallel trend for the growing adaptation of AI methods in finding asthma trigger events has been observed in the review work by [PERSON] et al. [1] spanning the last decade. Nevertheless, some limitations still exist, including biased algorithms, sharing of larger datasets, patients' privacy, and ethical considerations that need to be addressed.  
Figure 2 illustrates that most of the articles that qualified for this review were published in journals that focus on in-depth research and analytics. This review, conducted between 2019 and 2024, includes qualified articles that meet the criteria outlined in Table 1 and Figure 3. DL models have remained the most prominent over the years, consistently being favored for their performance. ML models, however, have gained increasing popularity over time, likely due to their computational efficiency, as they do not require powerful graphical processing units. Hybrid models have also seen a rise in usage, as they combine the strengths of both DL and ML. For example, DL models are often used to extract features, while ML models are employed for classification tasks. The miscellaneous category includes other statistical methods, such as knowledge graphs, linear discriminant analysis, and Gaussian mixture models, as well as review-based works.  
Figure 1: A hierarchical diagram illustrating the key components of asthma analysis, including data and AI methods, as highlighted during the review process.  
Figure 2: Proportion of research articles published in journals and conferences represented in line alongside the breakdown of ML, DL, hybrid, and Misc. (statistical) models depicted in bars with years on x-axis and count on y-axis.