The number of manuscripts related to radiomics, machine learning (ML), and artificial intelligence (AI) submitted to Radiology has dramatically increased in only a few years. Just walking through the RSNA 2017 Machine Learning Pavilion, one couldn’t help but wonder if all the noise pointed to CAD on steroids or to technology that is so far out there it belongs in the next Star Wars movie.. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. For decades, medical images have been generated and archived in digital form. Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. However, developing CAD applications is a multi-step, time consuming, and complex process. One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. AI currently outperforms humans in a number of visual tasks including face recognition, lip reading, and visual reasoning. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. As expected, the number of published articles in Radiology on these topics has also increased, now representing about 25% of publications in the past year. Despite this importance, limitations of modern radiology coupled with dizzying advances in AI are converging to drive automation in the field. Now, breakthroughs in computer vision also open up the possibility for their automated interpretation. There is much hype in the discussion surrounding the use of artificial intelligence (AI) in radiology. But the reality is, there are some real nuggets of hope in the gold mine. There is a head-spinning amount of new information to get under your belt before you can get started. While the use of artificial intelligence (AI) could transform a wide variety of medical fields, this applies in particular to radiology. Images obtained by MRI machines, CT scanners, and x-rays, as well as biopsy samples, allow clinicians to see the inner workings of the human body. Their results, published in Academic Radiology, concluded that access to a patient’s backstory does not hamper a radiologist’s work in most instances. Are you interested in getting started with machine learning for radiology? And now, it seems, we can add radiology to the list. For the last several years, artificial intelligence (AI) has represented the newest, most rapidly expanding frontier of radiology technology. However, radiology has been applying a form of AI – computer-aided-diagnostics (CAD) – for decades. The AI applications that are emerging now are no better and no worse than the CAD ones. 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