Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care

Laren D Tan,1,2 Nolan Nguyen,3 Enrique Lopez,1 Daniel Peverini,4 Mathew Shedd,1 Abdullah Alismail,1,2,5 H Bryant Nguyen1 1Department of Medicine, Loma Linda University Health, Loma Linda, CA, USA; 2Department of Cardiopulmonary Sciences, Loma Linda University Health, Loma Linda, CA, USA; 3School of Medicine, Loma Linda University Health, Loma Linda, CA, USA; 4Department of Medicine, University of New Mexico Consortium of the Americas for Interdisciplinary Science, Albuquerque, NM, USA; 5Department of Cardiopulmonary Sciences, Rush University, Chicago, IL, USACorrespondence: Laren D Tan, Department of Medicine, Loma Linda University Health, 11234 Anderson Street, Loma Linda, CA, 92374, USA, Email [email protected]: Asthma, a chronic respiratory condition, impacts over 339 million individuals globally, including 25 million in the United States, contributing to significant morbidity and healthcare costs. Despite advances, challenges persist in managing exacerbations, ensuring medication adherence, and patient education. This narrative review explores the transformative potential of artificial intelligence (AI) in improving asthma management through predictive analytics, personalized treatment, and continuous patient engagement. A search of the United States National Library of Medicine’s PubMed database was performed for articles pertaining to asthma and artificial intelligence, machine learning (ML), neural network, or deep learning. The current research on AI applications in asthma care was then reviewed, including algorithms, AI-driven tools for personalized medicine, and digital platforms for patient engagement. Case studies and clinical trials assessing AI’s impact on predictive accuracy and treatment adherence were reviewed. AI, particularly ML, enhances asthma management by analyzing data from wearables and patient records to predict exacerbations, stratify risk, and inform personalized treatment. Studies demonstrate AI’s capability to recommend tailored interventions, monitor adherence through smart applications, and facilitate real-time treatment adjustments. Ethical challenges include ensuring patient trust, data security, and equitable technology access. In conclusion, AI’s integration in asthma care holds significant promise for predictive interventions, personalized regimens, and continuous support, ultimately aiming to improve patient outcomes and reduce healthcare burdens. Continued advancements in AI will bridge current care gaps, fostering a patient-centric, proactive approach in asthma management.Keywords: artificial intelligence, asthma, machine learning, personalized medicine