Artificial Intelligence Methods and Digital Intervention Strategies for Predicting and Managing Chronic Obstructive Pulmonary Disease Exacerbations: An Umbrella Review

Background: Chronic Obstructive Pulmonary Disease (COPD) is a major global health burden in which acute exacerbations accelerate progression and increase hospitalizations. Emerging technologies, such as wearable biosensors, artificial intelligence (AI), and digital health tools, enable more proactive disease management. Objectives: This umbrella review synthesized evidence from systematic reviews and meta-analyses on (1) AI-driven prediction of COPD exacerbations using low-cost wearable biosignals, and (2) the effectiveness of digital health interventions on disease management, quality of life, and medication adherence. Methods: A systematic search of PubMed, Scopus, and Web of Science (2015–2025) identified eligible reviews. Methodological quality was assessed using AMSTAR-2, and study overlap was quantified with the Corrected Covered Area (CCA). A narrative synthesis was conducted across two research questions. Protocol registered in PROSPERO (CRD420251164450). Results: Twenty-seven reviews met the inclusion criteria. AI models demonstrated promising internal predictive accuracy but lacked external validation and clinical integration. Digital health interventions, such as mHealth applications and telerehabilitation, showed small to moderate improvements in quality of life and physical function. Reported effects varied considerably (OR = 0.20–2.37; I² = 0–94%), indicating substantial heterogeneity across studies. Evidence for improvements in medication adherence and exacerbation reduction was inconsistent, and most included reviews were rated “Low” or “Critically Low” in methodological quality, limiting the generalizability of findings. Conclusions: AI and digital tools show strong promise for proactive COPD management, particularly through wearable-derived biosignals, outperforming traditional static assessments. However, their clinical readiness remains limited due to small-scale studies, interpretability challenges, inconsistent outcome measures, and a lack of external validation. To support real-world translation and regulatory adoption, future research must prioritize large-scale, rigorous, and equitable studies with standardized methodologies and robust generalizability testing.