A tsunami of medical imaging procedures – all with higher image counts and resolutions to analyze – are drowning the limited number of radiologists available to interpret them. This makes their chosen healthcare profession increasingly difficult. This abundance of medical imaging data can be put to work to train today's efficient Convolutional Deep Neural Network models[1] running on the latest NVIDIA GPU processors to assist clinicians in their diagnostic tasks. And none too soon, because all that medical image data must be read by increasingly overwhelmed diagnostic clinicians. For the last decade, most of the focus in artificial intelligence (AI) has been on GPU processing, and rightfully so, with all the advancements going on there.