Artificial Intelligence in Diagnostic Imaging: Enhancing Accuracy and Efficiency (The Science Post, 2025)
The integration of Artificial Intelligence (AI), particularly deep learning algorithms, into diagnostic imagingrepresents a paradigm shift in radiological practice. This paper examines the potential of AI-powered tools to augmentthe capabilities of radiologists by improving diagnostic accuracy, reducing interpretation times, and identifying subtlepatterns beyond human perception. Convolutional Neural Networks (CNNs) are at the forefront of this revolution,demonstrating exceptional performance in detecting anomalies in mammography, computed tomography (CT), andmagnetic resonance imaging (MRI). This research outlines a methodology for developing and validating a CNNmodel for the detection of pulmonary nodules in chest CT scans. The results indicate a significant increase indetection sensitivity and a decrease in false-negative rates compared to traditional reading methods. The discussionaddresses challenges related to dataset curation, model generalizability, and the critical role of human-AIcollaboration, concluding that AI is poised to become an indispensable second reader in the radiology workflow