Abstract: Early detection of pulmonary nodules is crucial for improving lung cancer prognosis. Deep learning (DL) algorithms have emerged as promising tools for automated nodule detection in chest CT scans, offering potential to enhance diagnostic accuracy and reduce radiologist workload. This revie more...
Abstract: 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 more...
Abstract: Background: Artificial intelligence (AI) is increasingly integrated into medical imaging, offering potential improvements in diagnostic accuracy, workflow efficiency, and patient care. However, successful adoption depends on healthcare professionals’ perceptions, trust, and willingness to use more...
Abstract: Artificial intelligence (AI) is rapidly transforming medical imaging by enabling automated interpretation of images, workflow optimization, and advanced quantitative analysis. Over the past decade, AI techniques, including machine learning, deep learning, and convolutional neural networks, have demo more...
