COMPUTER VISION AND AI IN MEDICAL IMAGING: ENHANCING DIAGNOSTIC ACCURACY IN RADIOLOGY

Abstract

The integration of Computer Vision and Artificial Intelligence (AI) in medical imaging has revolutionized the field of radiology by significantly improving diagnostic accuracy, speed, and efficiency. This article explores the transformative potential of AI-driven image analysis systems, particularly convolutional neural networks (CNNs), in detecting and classifying abnormalities such as tumors, fractures, and pulmonary diseases. The study reviews existing AI frameworks, discusses clinical case studies from Pakistan, and presents challenges like data privacy, algorithm bias, and integration with clinical workflows. By combining deep learning algorithms with large-scale imaging datasets, radiology is transitioning towards a more precise and personalized healthcare paradigm.