A review of artificial intelligence-based research on chronic obstructive pulmonary disease.
Clinical Snapshot
PICO Framework
| P — Population | Patients with chronic obstructive pulmonary disease (COPD) |
| I — Intervention | Artificial intelligence-based approaches (machine learning, deep learning) |
| C — Comparator | Traditional diagnostic and management methods |
| O — Outcomes | Diagnostic accuracy, disease grading, prognosis prediction, disease management outcomes |
Bottom Line
This narrative review examines AI applications in COPD management across diagnosis, grading, and prognosis. While covering a clinically relevant topic, the review lacks systematic methodology, with no described search strategy, inclusion criteria, or quality assessment. The authors, primarily from information management backgrounds, provide a broad overview of AI techniques but limited clinical interpretation. Without quantitative synthesis or rigorous methodology, this review offers general insights into the AI-COPD research landscape but insufficient evidence for clinical decision-making. Clinicians should await systematic reviews with robust methodology before considering AI tools for routine COPD management. The rapid evolution of AI technology and regulatory landscape, particularly regarding TGA approval for AI medical devices, requires more rigorous evidence synthesis to guide clinical adoption.
Key Findings
P Value: Not applicable for narrative review
Effect Size: Not quantified - narrative synthesis only
Primary Outcome: Applications of AI in COPD diagnosis and management
Nnt Or Sensitivity: Individual study performance metrics not synthesised
Confidence Interval: Not provided
Clinical Application
Implementation challenges not systematically addressed; technology availability and training requirements unclear Limited applicability assessment for Australian healthcare system; no consideration of TGA regulatory requirements for AI medical devices or integration with existing COPD management pathways COPD patients across all disease stages, though specific population characteristics not defined
Abstract
In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three major chronic diseases, has achieved remarkable progress in diagnosis, grading, and prognosis, which is of great significance for promoting the clinical transformation of respiratory diseases. To deeply explore the application of AI in the diagnosis and management of COPD, this paper reviews recent studies based on machine learning and deep learning, covering screening and diagnosis, disease grading and assessment, disease management and monitoring, and treatment. First, the technical basis of COPD-related research is analyzed from five perspectives: traditional research methods, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Then, the commonly used datasets and model evaluation metrics are summarized. Finally, the application scenarios of AI in COPD research are elaborated, focusing on three aspects: early screening and diagnosis, disease monitoring and risk prediction, and disease classification and risk stratification. This paper summarizes the main research hotspots at home and abroad over the past five years with representative models, and analyzes and compares the advantages and limitations of each type of model in specific COPD tasks through comparative experiments. The study also outlines prospects for the future development of this field, aiming to provide theoretical references and insights for subsequent research.
References
- 1.Abulizi, A., Zhou, J., Abudukelimu, N., Yehaiya, G., Abudukelimu, M., & Abudukelimu, H. (2026). A review of artificial intelligence-based research on chronic obstructive pulmonary disease. Respiratory Medicine, 108778. https://doi.org/10.1016/j.rmed.2026.108778
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