Intel and Philips recently tested two healthcare uses for deep learning inference models using Intel Xeon Scalable processors and the OpenVINO toolkit. One use case focused on X-rays of bones for bone-age-prediction modeling, the other on computed tomography (CT) scans of lungs for lung segmentation. In these tests, Intel and Philips achieved a speed improvement of 188 times for the bone-age-prediction model, and a 38 times speed improvement for the lung-segmentation model over the baseline measurements.