Product and Performance Information

1“Medical Imaging Market Analysis, Size, Trends,” MedSuite, 2016, https://idataresearch.com/product/medical-imaging-market-united-states/.
2

Lung Nodule Detection from CT Scan Using Intel® Processors, QuEST Global white paper, builders.intel.com/docs/aibuilders/lung-nodule-detection-from-ct-scan-using-intel-processors.pdf.

3

GE Healthcare’s AIRx™ Tool Accelerates Magnetic Resonance Imaging using Intel® AI Technologies, Intel white paper, intel.com/content/www/us/en/artificial-intelligence/solutions/gehc-airx.html.

4

Intel and GE Healthcare Partner to Advance AI in Medical Imaging,” Intel Customer Spotlight, intel.com/content/www/us/en/customer-spotlight/stories/ge-healthcare-medical-imaging.html.

5Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel® microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel® microprocessors. Certain optimizations not specific to Intel® microarchitecture are reserved for Intel® microprocessors. Please refer to the applicable product user and reference guides for more information regarding the specific instruction sets covered by this notice.
6System test configuration disclosure: Intel® Core™ i5-4590S CPU @ 3.00 GHz, x86_64, VT-x enabled, 16 GB memory, OS: Linux magic x86_64 GNU/Linux, Ubuntu 16.04 inferencing service docker container. Testing done by GE Healthcare, September 2018. Test compares TensorFlow model total inferencing time of 3.092 seconds to the same model optimized by the Intel® Distribution of OpenVINO™ toolkit optimized TF model resulting in a total inferencing time of 0.913 seconds.