In the area of diagnostic imaging, the chest X-ray (CXR) is a fundamental modality routinely used for diagnostic assessments, particularly in the identification of lung abnormalities. However, the reliability of CXR is often questioned due to limitations of spatial resolution and the diverse nature of human anatomy, with the difficulty of distinguishing diagnostic errors from opinion discrepancies among observers. The aim of this study is to evaluate the performance of an automatic deep learning-based detection (DLAD) algorithm in detecting pulmonary lesions on CXR images, particularly in a clinical setting characterized by low disease prevalence. As computer-aided detection (CAD) systems increasingly move from experimental research into clinical practice, this study serves as a critical examination of their effectiveness, specifically in real-world scenarios that differ from balanced conditions in a simulated environment, i.e., equal numbers of normal and abnormal scans [1,2].