IDEAS home Printed from https://ideas.repec.org/a/ajp/edwast/v9y2025i2p2353-2365id5088.html
   My bibliography  Save this article

The role of artificial intelligence and machine learning in asthma and chronic obstructive pulmonary disease management

Author

Listed:
  • Mahendra Aseri

Abstract

The purpose of this review is to establish the role that AI and ML play in managing asthma and Chronic Obstructive Pulmonary Disease (COPD), with particular emphasis on disease management across various stages, diagnosis, and treatment personalization through monitoring. Data extraction was performed from articles published between December 2018 and October 2024, accessed using Scopus, PubMed, and Google Scholar databases. Articles focusing on the use of AI/ML in the management of respiratory diseases in clinical settings and real-life contexts were included in this study. Theoretical models or reviews of non-respiratory applications of AI/ML were excluded. AI and ML-based technology advancements provide the possibility to extend asthma and COPD management with many improvements. Such technologies can enable early and accurate detection by using advanced imaging and data analysis techniques. AI-driven models, such as MLMP-COPD, far outweigh the usual approaches made for predicting death and exacerbations. Big data analytics and telemedicine help in the integration of diverse data sources. AI and ML have an entirely new dimension and could perform more effectively for the management of both asthma and COPD. Practical implications include integrating AI-driven tools in clinical settings to improve diagnostic accuracy, enhance treatment, and assist in remote patient monitoring.

Suggested Citation

  • Mahendra Aseri, 2025. "The role of artificial intelligence and machine learning in asthma and chronic obstructive pulmonary disease management," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(2), pages 2353-2365.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:2:p:2353-2365:id:5088
    as

    Download full text from publisher

    File URL: https://learning-gate.com/index.php/2576-8484/article/view/5088/1884
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ajp:edwast:v:9:y:2025:i:2:p:2353-2365:id:5088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Melissa Fernandes (email available below). General contact details of provider: https://learning-gate.com/index.php/2576-8484/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.