A REVIEW OF ARTIFICIAL INTELLIGENCEBASED RESEARCH ON CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Chronic Obstructive Pulmonary Disease (COPD) has become a major focus of artificial
intelligence (AI) research in respiratory medicine, driving innovations in diagnosis, disease
management, and prognostic assessment. This review synthesizes recent AI‑based studies on
COPD, highlighting applications in medical imaging analysis, remote patient monitoring,
and non‑invasive diagnostic alternatives to traditional spirometry. AI algorithms are used to
quantify emphysema, assess fissure integrity, detect bronchiectasis, and analyze pulmonary
vessels from CT scans, enhancing radiologic evaluation. Wearable sensor data processed by
AI enable continuous remote monitoring of COPD patients, facilitating early detection of
exacerbations.
Novel AI tools also analyze respiratory sound attributes and voice features to diagnose
COPD, exemplified by the smartphone app *Swaasa*, which achieves ≈90% accuracy in
detecting COPD and tuberculosis from cough recordings. Another model, *AutoCOPD*,
accurately identifies COPD using only 10 quantitative CT features, demonstrating the
potential for streamlined imaging analysis. AI‑driven predictive models forecast disease
progression, exacerbation risk, and mortality, supporting personalized treatment plans and
optimizing clinical decision‑making.
The 2026 GOLD COPD Update emphasizes earlier and more precise diagnosis, positioning
AI as a key enabler of this shift toward proactive, individualized care. Implementation
challenges include ensuring data quality, model interpretability, and seamless integration
into existing clinical workflows. Future research should focus on developing robust,
validated AI systems to improve COPD management and patient outcomes globally.
Keywords: Chronic Obstructive Pulmonary Disease (COPD), artificial intelligence (AI),
respiratory medicine, machine learning.