Revolutionizing COPD and Asthma Management with Artificial Intelligence
Description
Revolutionizing COPD and Asthma Management with Artificial Intelligence Yudi Kurniawan Budi Susilo1*, Shamima Abdul Rahman2, 1Faculty of Business and Technology, University of Cyberjaya, 63000 Cyberjaya , Selangor, Malaysia 2Centre for Research and Graduate Studies, University of Cyberjaya, 63000 Cyberjaya , Selangor, Malaysia Authors’ email: yudi299@gmail.com*, shamima@cyberjaya.edu.my *Corresponding author Abstract: The integration of artificial intelligence (AI) into the management of chronic obstructive pulmonary disease (COPD) and asthma offers significant advancements in patient care, diagnosis, and treatment personalization. AI technologies, particularly machine learning and deep learning, have shown great promise in predictive modeling, enabling earlier detection and more accurate diagnoses. AI-driven tools, such as telemedicine and remote monitoring applications, are helping clinicians provide personalized care and manage these conditions more effectively, especially for high-risk patients. Additionally, AI's ability to analyze vast datasets, including electronic health records and medical imaging, allows for more dynamic and adaptive treatment plans tailored to individual patients' needs. However, challenges remain, such as ensuring data privacy, addressing ethical concerns, and developing standardized regulatory frameworks to support the implementation of AI in clinical settings. Despite these challenges, the potential of AI to revolutionize COPD and asthma management is vast, and continued research and collaboration will be critical in overcoming these barriers and enhancing healthcare outcomes for patients worldwide. Keyword: Artificial Intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), Asthma Management, Machine Learning (ML), Remote Monitoring
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The initial search yielded 2,420 records, which were subsequently screened. After removing 1,508 records that did not meet the inclusion criteria, 912 records were included for bibliometric analysis. The inclusion criteria were based on relevance to the specified topics, document type (articles), language (English), and publication stage (final). The exclusion criteria removed records that did not align with these parameters. The analysis utilized specialized software tools such as VOSviewer, Harzing’s Publish or Perish and Bibliometrix (R) to conduct citation analysis, keyword co-occurrence analysis, and author collaboration network mapping. These tools enabled the visualization of citation networks and the identification of significant research trends, providing insights into the development of AI applications in COPD and asthma management. Potential biases and limitations were acknowledged, including the reliance on Scopus as the sole database, which may affect the generalizability of the findings. The methodology adheres to established best practices, with appropriate citation of the PRISMA framework and relevant bibliometric studies, ensuring methodological transparency and rigor. The date of data extraction was March 16, 2025.
Institutions
- Cyberjaya University College of Medical Sciences