Traditional computer-aided detection (CAD) systems have supported radiologists in mammographic interpretation for over two decades, yet their contribution to diagnostic improvement has been limited by high false-positive rates and inconsistent performance across breast densities. Recent advances in artificial intelligence (AI), particularly deep learning–based CAD systems, have redefined image interpretation by enabling automated feature learning from large datasets. This manuscript provides a technical comparison of traditional CAD and AI-based CAD in mammography, focusing on diagnostic accuracy, workflow efficiency, and robustness across diverse imaging environments. Evidence shows that AI-based CAD demonstrates superior sensitivity, specificity, false-positive reduction, and radiologist support, reflecting substantial progress toward reliable early breast cancer detection. Despite challenges involving transparency, dataset bias, and clinical implementation, AI-based CAD is positioned to become an essential component of modern screening programs.