2026-03-18 (三) 16:30 ~ 17:30

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive respiratory disorder characterized by airflow limitation, structural lung changes, and diverse clinical phenotypes. Traditional diagnostic and management approaches rely on pulmonary function tests, imaging interpretation, and clinical judgment, which may not fully capture disease complexity or predict individual outcomes. In recent years, artificial intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a powerful tool to enhance COPD assessment and management. AI-based algorithms enable automated analysis of high-resolution computed tomography (HRCT) to quantify emphysema, airway remodeling, and air trapping with high reproducibility, providing objective imaging biomarkers of disease severity and progression. In addition, AI models integrating clinical data, spirometry, imaging, and biomarkers can improve risk stratification, predict exacerbations, and support personalized treatment strategies. AI applications have also shown promise in phenotyping COPD, identifying distinct subgroups with different prognoses and therapeutic responses. Furthermore, AI-driven decision support systems may assist clinicians in early detection, longitudinal monitoring, and optimization of therapy. Despite these advances, challenges remain, including data quality, model interpretability, and generalizability across populations. Overall, AI represents a transformative approach in COPD care, with the potential to improve diagnostic precision, prognostic accuracy, and individualized patient management.