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Promoting Health for All with Artificial Intelligence

Technology can enrich the life of every person, especially when it has the potential to help prevent, treat, and cure disease. Intel is working with leaders in the ecosystem to revolutionize health and life sciences, whether it’s accelerating drug discovery to speed pharmaceutical development or improving healthcare access and affordability. The use of artificial intelligence (AI) in healthcare—including computer vision, machine learning, and deep learning—plays a critical role in this goal. Combined with a strong infrastructure for data management, AI can help researchers and health systems quickly gather insights from massive amounts of data that were previously inaccessible due to data silos.

How Is AI Being Used in Healthcare?

AI can make it possible for automated systems to evaluate medical images for anomalies, monitor patient vital signs at scale, and alert clinicians to intervene when needed. It helps improve operational and clinical workflows and integrate data from many different sources so that clinicians can make more-informed decisions. Researchers are tapping AI to assist in drug discovery, targeted therapeutics, and infectious disease management. Other examples of AI in healthcare and life sciences include lab automation, robotics, and AI-enabled telemedicine.

Benefits of AI in Healthcare

AI improves productivity by automating tasks and can help clinicians with fast, accurate diagnoses and treatment.2 Artificial intelligence in radiology can reduce the compute time needed to generate images. In population health, machine learning can help identify the likelihood of hospital readmission. AI in pharmaceuticals development can lead to the discovery of new drugs. AI can also make it possible to ingest data from multiple sources, like medical records and vital signs, and identify patterns that are difficult for humans to spot.

Intel AI in Healthcare and Life Sciences

Intel’s work in AI is helping health industry experts address some of the most pressing challenges today. These include:

  • Precision medicine – AI can make sense of unstructured and structured health data, such as genomics data sets, that are crucial to advancing precision medicine, an approach to care centered on the patient’s unique genome and health information.
  • Clinical systems – AI can help transform raw data into new insights that inform treatment plans at every stage of the patient’s journey. It can also support care-at-a-distance strategies, such as telehealth and robotics, applied across inpatient and outpatient environments.
  • Pharmaceutical processes – AI can play a major role in drug development, transforming compound discovery.
  • Medical imaging – AI can enhance medical image quality and assist clinicians in evaluating images quickly and accurately.

Intel offers a range of flexible, scalable, open hardware to fit every compute need, from low-power VPUs to high-performance CPUs. And software tools like the Intel® Distribution of OpenVINO™ toolkit remove the complexity of working with different hardware back ends, so you can write code once and deploy it everywhere.

Samsung Collaborates with Intel to Speed Nerve Detection and Improve Workflows

Samsung Medison has collaborated with Intel to speed nerve detection and improve workflows. NerveTrack uses the Intel® Distribution of OpenVINO™ toolkit to help detect and identify nerves during ultrasounds. The real-time inference of ultrasound nerve images is helping improve accuracy for anesthesiologists when searching for hard-to-find nerves.

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Notices and Disclaimers

Software and workloads used in performance tests may have been optimized for performance only on Intel® microprocessors.

Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.

Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.

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Product and Performance Information

1Intel® 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.
2

“The potential for artificial intelligence in healthcare,” June 2019, Future Healthcare Journal, ncbi.nlm.nih.gov/pmc/articles/PMC6616181/.

3

“Advantages and limitations of total laboratory automation: a personal overview,” Clinical Chemistry and Laboratory Medicine (CCLM), February 2019, degruyter.com/view/journals/cclm/57/6/article-p802.xml.

4Configurations: Original model was trained using TensorFlow 1.6 for Python 2.7 without Intel® optimizations and converted by GE Healthcare to OpenVINO™ 2018 R4. Hardware and configurations used for testing: GE Gen6-P image compute node 3.10.0-862.el7.x86_64; processor: Intel® Xeon® processor E5-2680 v3; speed; 2.5 GHz; cores: 12 cores per socket, Docker container has access to 22 CPU cores; sockets: two; RAM: 96 GB (DDR4); hyperthreading: enabled; security updates: Spectre and Meltdown updates applied. Software used for testing: TensorFlow version: 1.6 without Intel® MKL-DNN optimizations; Gcc version: 2.8.5; Python version: 2.7; OpenVINO™ version: 2018 R4 (model server v0.2); OS: HeliOS 7.4 (Nitrogen).
5System 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.