Research Information System
International Journal of Medical Informatics, vol.211, 2026 (SCI-Expanded, Scopus)
Introduction: Artificial intelligence (AI) technologies are increasingly being integrated into pulmonary rehabilitation (PR) to improve individualization, real-time monitoring, and adherence in individuals with chronic respiratory diseases. However, their clinical impact on exercise capacity remains unclear. This systematic review and meta-analysis aimed to evaluate the effectiveness of AI-supported PR programs compared to usual care in improving exercise capacity and respiratory function in adults with chronic respiratory diseases. Methods: This systematic review and meta-analysis followed PRISMA guidelines and was registered with PROSPERO (ID: CRD420251075622). A comprehensive search was conducted across five electronic databases (PubMed, Web of Science, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL) and PEDro) from inception to July 2025. Statistical analyses for the meta-analysis were conducted using RevMan 5.4. Results: Three eligible RCTs with a total of 456 participants were included. Pooled analysis showed a significant improvement in 6-minute walk distance (6MWD) after AI-assisted PR group compared to control (MD: 22.08 m; 95% CI: 4.96–39.20; p = 0.01). Moderate heterogeneity was observed (I2 = 40%). No meta-analysis was conducted for respiratory function due to insufficient pre-post data. Risk of bias was generally low, though participant blinding was absent in all studies. Methodological quality was good, with a mean PEDro score of 6.0 ± 0.82. Conclusion: AI-supported PR can significantly improve exercise capacity in individuals with chronic respiratory diseases. Despite promising results, high-quality studies in different pulmonary patient groups are needed to address existing limitations, particularly regarding standardization, cost-effectiveness, and clinical integration of AI-technology.