Artificial Intelligence in Radiology and the Growing Applications of Medical Imaging

Artificial intelligence (AI) in the field of radiology has seen rapid growth, with the success of deep learning. The use of computed tomography (CT), magnetic resonance imaging (MRI), nuclear medicine, and picture archiving and communication systems (PACS) is just a few examples of how computers have changed diagnostic imaging. With the advancement in deep learning, AI systems are now able to recognize and localize complex imaging patterns from different radiological modalities. In certain applications, their performance is now comparable to that of human experts. This has sparked a lot of interest in using AI to improve radiology workflow, increase productivity, and attain more consistency in diagnosis [1]. Chest X-rays (CXRs) are among the most frequently requested imaging studies worldwide. However, overlapping structures and subtle disease patterns make interpretation difficult. The release of large-scale datasets like CXR14, CheXpert, MIMIC-CXR, and PadChest has significantly accelerated the development of AI-based systems. These datasets were obtained from PACS archives and radiology reports and used to train convolutional neural networks (CNNs) for disease classification an