There is much hype in the discussion surrounding the use of artificial intelligence (AI) in radiology. Are you interested in getting started with machine learning for radiology? The constellation of new terms can be overwhelming: Deep Learning, TensorFlow, Scikit-Learn, Keras, Pandas, Python and Anaconda. 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. Radiology generates a huge amount of digital data as obtained images are included into patients’ clinical history for diagnosis, treatment planning, screening, follow up, or prognosis. And now, it seems, we can add radiology to the list. One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. August 03, 2018 - Artificial intelligence and machine learning tools have the potential to analyze large datasets and extract meaningful insights to enhance patient outcomes, an ability that is proving helpful in radiology and pathology.. AI currently outperforms humans in a number of visual tasks including face recognition, lip reading, and visual reasoning. 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. However, developing CAD applications is a multi-step, time consuming, and complex process. Now, breakthroughs in computer vision also open up the possibility for their automated interpretation. The AI applications that are emerging now are no better and no worse than the CAD ones. Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Despite this importance, limitations of modern radiology coupled with dizzying advances in AI are converging to drive automation in the field. There is a head-spinning amount of new information to get under your belt before you can get started. But the reality is, there are some real nuggets of hope in the gold mine. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. For decades, medical images have been generated and archived in digital form. 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. While the use of artificial intelligence (AI) could transform a wide variety of medical fields, this applies in particular to radiology. 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