Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.

backgroundExacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment.

methodsThis narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance.

resultsThree major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7-14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure-adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV

conclusionsAI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity.