Purpose <p>Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that significantly impacts global health. This study aims to comprehensively analyze global trends and research hotspots in the application of artificial intelligence (AI) for computed tomography (CT) detection of COPD.&#xa0;</p> Methods <p>Publications related to AI applications for CT detection in COPD from 2012 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. Bibliometric analysis was conducted using VOSviewer, CiteSpace, and the R package “bibliometrix”.</p> Result <p>The field has experienced publications growth, with 189 publications and an annual growth rate of 37.83%. The United States led with 53 publications, followed by China (51) and Germany (13). The University of Iowa was the most prolific institution (69), followed by Harvard University (47) and Brigham and Women’s Hospital (37). Hoffman Eric A. is the most prolific author with 16 publications, and journals such as <i>Scientific Reports</i> and <i>Radiology</i> were key contributors to the field. Emerging topics included “quantitative imaging”, “low dose CT”, “pulmonary disease”, “body mass index”, “subpopulations”, and “prevalence”, suggested growing interest in comprehensive patient assessment and population studies.&#xa0;</p> Conclusion <p>This bibliometric analysis provides a comprehensive overview of research on AI applications for CT detection of COPD from 2012 to 2024, identifying key contributors, research hotspots, and emerging trends. Future research should focus on differentiating COPD from other lung diseases or COPD subpopulations for personalized treatment.</p> Clinical trial number <p> not applicable.</p>