Purpose or Case Report: In 2017, roughly 2 trillion (2,000,000,000,000) medical images were produced, reviewed, reported, archived, and used in the detection and management of disease. Based on historical trends, this number has doubled every 5 years and is accelerating. This explosive growth in imaging data has created major opportunities for the use of Artificial Intelligence (AI). The question is less whether radiologists, and technologists, will be replaced by AI (they will not) and more about whether we could survive without AI. Although intelligent algorithms have been used for some time in segments of the imaging field, new methods of machine learning, based particularly on “deep learning”, are much more powerful. Many of the deep learning publications today point to the promise of significant advances in efficiency, precision, reproducibility, and prognostic abilities.
If AI will not replace radiologists/technologists but rather augment them with tools to meet the rising demands for diagnostic imaging, then it is imperative that we have a basic understanding of the concepts and language that defines this area of knowledge. In the not so distant past the average technologist understood the basics of film processing but wouldn’t even recognize the words DICOM or EMR; we are now at that point of change with AI. Deep learning, machine learning, neural networks, ground truth, the list goes on. The goal of this presentation is to provide a basic framework of the concepts, terminology, and references to how AI has, and likely, will be employed in medical imaging, thus making us better practitioners and partners with this technology.

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