Pediatric Asthma Management Using Smart Inhaler Monitoring And Artificial Intelligence Systems

Abstract

Background: Pediatric asthma is one of the most common chronic respiratory diseases in children, and it is often associated with poor symptom control, non-adherence to medications, and frequent hospital visits. Recent advances in smart inhaler technologies and artificial intelligence (AI) present new opportunities for personalized asthma management. Objective: This study is designed to evaluate the impact of incorporating smart inhaler monitoring systems with AI-based predictive analytics on the enhancement of asthma control and treatment adherence in pediatric patients.  Methodology: Prospective observational study was conducted on 250 children of age group 6-17 years diagnosed with asthma. Smart inhalers tracked medication usage in real-time, and AI algorithms used inhaler usage data, environmental factors, and symptom reports to predict exacerbation risk. Clinical outcomes were evaluated over a 12-month period.  Findings: The smart inhaler system with AI capabilities increased medication adherence from 68.4 per cent to 89.7 per cent. There was an association between increased use of inhaled corticosteroids and 34.8% reductions in asthma exacerbations and 29.5% reductions in emergency department visits. The predictive model was 92.1% accurate in detecting high-risk asthma episodes, with 90.4% sensitivity and 93.6% specificity. Patient and caregiver satisfaction was met in 94.2%.  Conclusion: Smart inhaler monitoring with AI holds great promise for improving pediatric asthma care through adherence improvement, early intervention, and asthma complication reduction. These findings support the implementation of intelligent digital health systems in pediatric respiratory care.