A narrative review of artificial intelligence and computational modeling in Chronic Obstructive Pulmonary Disease (COPD) finds that AI may improve phenotyping, diagnosis, prognosis, exacerbation prediction, and treatment optimization — but persistent problems with data quality, algorithmic bias, interpretability, and limited clinical validation remain major barriers to practice. The review, reported in the spirit of PRISMA-ScR, synthesizes technical, clinical, and policy evidence across mechanistic, multiscale, and machine learning approaches.

Why It Matters To Your Practice

• AI tools for COPD are expanding beyond diagnosis into prognosis, personalized regimen selection, exacerbation prediction, and drug discovery.

• For clinicians, the key issue is not whether models can generate predictions, but whether those predictions are reliable, explainable, and generalizable to real-world patients.

• The review suggests current evidence is promising but not yet sufficient for broad, uncritical adoption in routine care.

Clinical Implications

• Expect the near-term value of AI in COPD to be strongest in decision support, risk stratification, and phenotyping rather than fully autonomous management.

• Before using an AI-enabled tool, clinicians should look for external validation, transparency about training data, and evidence that performance holds across populations and care settings.

• Bias, poor data quality, and limited explainability could worsen disparities or reduce trust if tools are deployed without adequate oversight.

Insights

• The review takes a cross-modal approach, covering pathophysiology, diagnostic and prognostic use cases, treatment optimization, exacerbation prediction, and drug repurposing.

• It evaluates mechanistic, multiscale, and machine learning models together, highlighting how these methods may complement each other in precision management.

• Strategic priorities include multimodal data integration, explainable AI, and more rigorous clinical validation and implementation research.

The Bottom Line

• AI in COPD shows broad potential, but the field is still constrained by bias, interpretability concerns, and validation gaps.

• Clinicians should view current tools as emerging aids that require careful scrutiny before integration into routine practice.

• The biggest opportunity is not just better prediction, but translating computational advances into measurable improvements in diagnosis, treatment, and outcomes.