Artificial intelligence (AI) is an incredibly powerful tool for building computer vision systems that support the work of radiologists. Over the last several years, artificial intelligence methods have revolutionized the analysis of digital images, leading to high interest and explosive growth in the use of “deep” machine learning and other AI methods to analyze clinical images. These promising techniques create computer vision systems that perform some image interpretation tasks at the level of expert radiologists. Deep learning methods are now being developed for image reconstruction, imaging quality assurance, imaging triage, computer-aided detection, computer-aided classification, radiogenomics, and other new imaging insights. The resulting computer vision systems have the potential to provide real-time assistance to imaging professionals, thereby reducing diagnostic errors, improving patient outcomes, and reducing costs. We will review the origins of AI and its applications to medical imaging, define key terms, and show examples of real-world applications that suggest how AI may change the practice of radiology. We will also review key shortcomings and challenges that may limit the application of AI to radiology.
