Artificial Intelligence in Diagnostic Imaging: Enhancing Accuracy and Efficiency

The integration of Artificial Intelligence (AI), particularly deep learning algorithms, into diagnostic imaging represents a paradigm shift in radiological practice. This paper examines the potential of AI-powered tools to augment the capabilities of radiologists by improving diagnostic accuracy, reducing interpretation times, and identifying subtle patterns 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), and magnetic resonance imaging (MRI). This research outlines a methodology for developing and validating a CNN model for the detection of pulmonary nodules in chest CT scans. The results indicate a significant increase in detection sensitivity and a decrease in false-negative rates compared to traditional reading methods. The discussion addresses challenges related to dataset curation, model generalizability, and the critical role of human-AI collaboration, concluding that AI is poised to become an indispensable second reader in the radiology workflow.