Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques—A Review

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition, contributing significantly to global morbidity and mortality. Traditional diagnostic tools are effective in diagnosing COPD. However, these tools demand specialized equipment and expertise. Advances in artificial intelligence (AI) provide a platform for enhancing COPD diagnosis by leveraging diverse data modalities. The existing reviews primarily focus on single modalities and lack information on interpretability and explainability. Thus, this review intends to synthesize the AI-powered frameworks for COPD identification, focusing on data modalities, methodological innovation, evaluation strategies, and reporting limitations and potential biases. By adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across multiple repositories. From an initial pool of 1978 records, 22 studies were included in this review. The included studies demonstrated exceptional performance in specific settings. Most studies were retrospective and limited in diversity, lacking generalizability and external or prospective validation. This review presents a roadmap for advancing AI-assisted COPD detection. By highlighting the strengths and limitations of existing studies, it supports the development of future research. Future studies can utilize the findings to build models using prospective, multicenter, and multi-ethnic validations, ensuring generalizability and fairness.