Chronic Obstructive Pulmonary Disease (COPD) remains a serious health problem in the world with increasing prevalence, underdiagnosis, and growing socioeconomic burden, particularly in low and middle-income countries. This perspective presents the shifting pattern of epidemiology that is attributed to both the smoking and non-smoking exposure risk factors, including bio-mass exposures, air pollution, and occupational risks. The recent advances of diagnostic methods (from high-resolution imaging to biomarker-targeted) are enhancing the earlier detection of the condition and the further characterization of types of COPD. The rate at which patients can be screened earlier, exacerbations predicted, and disease management personalized has further improved with new Artificial Intelligence (AI) systems, including machine-learning algorithms, medical signal analysis, and Internet of Medical Things (IoMT)-based systems. Some of the therapeutic innovations being developed include innovative inhaled therapy, Type-2 inflammatory biologics, and combined digital health capabilities, capable of supporting remote monitoring and self-management. Despite all this, significant differences in access to diagnostics, adherence to the guidelines, and equal access to innovative treatments still exist. To reduce the COPD burden in the world and improve patient outcomes, it is significant to improve precision-medicine strategies, augment digital health interventions, and focus on prevention.
Sharma et al. (Tue,) studied this question.