The Evolving Landscape of COPD Typization

Chronic obstructive pulmonary disease (COPD) represents an escalating global health challenge characterized by profound clinical and biological heterogeneity. Conventional diagnostic paradigms, primarily reliant on spirometric criteria and broad phenotypic labels, often fail to capture the complex molecular mechanisms underlying effective precision medicine. This narrative review synthesizes the evolving landscape of COPD characterization, analyzing the integration of biomarkers, advanced quantitative imaging, and multi-omics technologies. Key developments highlighted include the clinical validation of biologics targeting type 2 inflammation, which reinforce the paradigm shift from generic symptomatic management toward the identification of specific treatable traits. We further explore the role of artificial intelligence and deep learning in enhancing radiological precision and body composition analysis. Ultimately, this work proposes a transition toward a GETomics (Genetics, Environment, and Time) framework as a fundamental prerequisite for transcending the limitations of traditional classification systems and delivering truly personalized care in the 21st century.