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Published By: Intel     Published Date: Apr 11, 2019
Intel teamed up with Philips to show that servers powered by Intel® Xeon® Scalable processors could be used to efficiently perform deep learning inference on patients’ X-rays and computed tomography (CT) scans, without the need for accelerators. The ultimate goal for Philips is to offer artificial intelligence (AI) to its end customers without significantly increasing the cost of the customers’ systems and without requiring modifications to the hardware deployed in the field.
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Intel
Published By: Intel     Published Date: Apr 11, 2019
Hospitals produce 50 petabytes of data per year.1 A staggering 90 percent of that is medical imaging, and 50 percent of medical imaging data is from X-ray scanners.2 This is a flood of data for radiology departments, resulting in potentially long turn-around-time (TAT)—even when the scan is considered stat. In critical conditions, such as pneumothorax, escalation of interpretation is essential to accelerate treatment.
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Intel
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