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Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.
Abdul Rahaman Wahab Sait1,2, Mujeeb Ahmed Shaikh2,3
1Department of Archives and Communication, Center of Documentation and Administrative Communication, King Faisal University, P.O. Box 400, Hofuf 31982, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|October 29, 2025
Summary
Artificial intelligence (AI) can enhance chronic obstructive pulmonary disease (COPD) diagnosis using various data. This review synthesizes AI frameworks for COPD detection, identifying limitations and proposing future research directions for improved generalizability and fairness.
Area of Science:
- Respiratory Medicine
- Medical Informatics
- Artificial Intelligence
Background:
- Chronic obstructive pulmonary disease (COPD) is a major global health burden, with traditional diagnostics requiring specialized resources.
- Artificial intelligence (AI) offers potential for improved COPD diagnosis by integrating diverse data modalities.
- Existing reviews often focus on single data types and lack insights into AI model interpretability and explainability for COPD.
Purpose of the Study:
- To systematically review AI-powered frameworks for COPD identification.
- To analyze data modalities, methodological innovations, and evaluation strategies in AI-driven COPD diagnosis.
- To identify reporting limitations, potential biases, and future research needs in AI-assisted COPD detection.
Main Methods:
- Systematic literature search adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
- Inclusion of 22 studies from an initial pool of 1978 records.
- Analysis focused on data modalities, AI methodologies, performance evaluation, and study limitations.
Main Results:
- AI frameworks show high performance in specific COPD detection contexts.
- Most reviewed studies were retrospective, lacking diversity and external/prospective validation.
- Significant limitations were identified regarding generalizability and potential biases in current AI models for COPD.
Conclusions:
- AI holds promise for enhancing COPD diagnosis, but current research faces generalizability challenges.
- There is a need for prospective, multicenter, and multi-ethnic validation of AI models.
- Future research should prioritize developing fair and broadly applicable AI-assisted diagnostic tools for COPD.