Artificial intelligence and IoT-enabled approaches for COPD detection and monitoring: a systematic literature review

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Conventional approaches to COPD diagnosis and management rely on periodic clinical assessments, limiting opportunities for early detection and continuous patient monitoring. Recent advances in Artificial Intelligence (AI) and Internet of Things (IoT) technologies have enabled intelligent, data-driven healthcare solutions for COPD detection and management. This systematic literature review synthesizes current evidence on AI- and IoT-enabled approaches for COPD detection and management, and it identifies existing research gaps and implementation challenges. This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive systematic literature review was performed across PubMed, IEEE Xplore, and Google Scholar for studies published between January 2015 and May 2026. Following duplicate removal and a two-stage screening process using predefined eligibility criteria, 32 peer-reviewed studies were included. The selected studies were synthesized under four COPD detection and management domains: early detection, exacerbation prediction, remote monitoring, and self-monitoring. The reviewed studies demonstrate that AI and IoT technologies can improve COPD detection and management through intelligent analysis of physiological and environmental data collected from wearable devices, smart sensors, and digital health platforms. Traditional machine learning algorithms and deep learning models showed promising performance for COPD detection and exacerbation prediction, while IoT-enabled systems supported continuous patient monitoring and self-monitoring. However, most studies focused on individual COPD management domains and were limited by small, single-center datasets and inadequate external validation. AI-IoT integration has considerable potential to transform COPD management by enabling continuous, personalized, and data-driven healthcare. Nevertheless, future research should prioritize the development of integrated, cost-effective systems that combine early detection, exacerbation prediction, remote monitoring, and self-monitoring within a unified framework, supported by large-scale multicenter validation to facilitate real-world clinical adoption. Registration number CRD420261448597 Date 14 Jul 2026.