Healthcare and Life Sciences Technology Solutions
Driving the Future of Healthcare and Life Sciences
At Intel, our decades-long history in healthcare and life sciences has given us deep insights into the needs of clinicians, researchers, and patients. We use this knowledge in combination with our expertise in AI, ubiquitous computing, pervasive connectivity, and edge-to-cloud capabilities to create technologies that help organizations overcome complex challenges and use data in more intelligent and effective ways.
With a vast hardware and software portfolio that supports a robust partner ecosystem, we’re powering the convergence of digital technologies into instruments, devices, and tools that can improve patient outcomes and experiences, accelerate scientific discoveries, and streamline clinical and lab workflows for providers and researchers. Intel® technology delivers the platform ubiquity and the performance, flexibility, and scalability needed to transform health and life sciences and help improve the life of every person on the planet.
Healthcare and Life Sciences Success Stories
GE Improves Cancer Diagnosis Accuracy
The GE Healthcare GE Invenia ABUS 2.0, powered by Intel® Core™ processors and third-party AI tools, improves the clinical accuracy of breast cancer lesion detection, providing up to 93 percent sensitivity for detection and reducing reading time by 33 percent1 2.
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TGen Cuts Sample Processing Time to Seven Hours3
Discover how high performance computing—enabled by Intel® Xeon® Scalable processors, Intel® Optane™ memory, and Dell rack servers—is helping in the development of genomic treatments for rare diseases.
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KFBIO Accelerates Cancer Screening by 8.4x4
Find out how KFBIO used Intel® AI technologies to accelerate automated screening for abnormalities in Pap smears by 8.4x.
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Samsung Improves Nerve Detection Accuracy by More Than 20%5
Learn how real-time AI inference of ultrasound nerve images helped accelerate image processing, improve workflows, and boost accuracy of nerve detection for anesthesiologists.
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AI in Healthcare and Life Sciences
AI-enabled technologies are shaping the future of healthcare and life sciences. Intel’s work in AI is helping professionals address pressing challenges, such as advancing precision medicine, transforming raw data into new insights, accelerating pharmaceutical development, and streamlining medical imaging workflows.
Powering Smart Hospitals
Intel enables smart hospitals by simplifying integration and enabling the intelligence, security, and connectivity required to modernize processes and transform patient care.
Smart Hospital Applications
Medical Imaging
Accelerate medical imaging and make breakthroughs in patient diagnosis and treatment happen quicker with Intel® technologies.
Robotics in Healthcare
Transform patient care, streamline clinical processes, and promote worker safety with Intel-powered medical robots.
AI in Telemedicine
Redefine remote care with AI and IoT to improve access to care, boost clinical efficiencies, and keep patients connected.
Predictive Analytics
Fuel predictive analytics solutions that help improve outcomes and reduce costs with big data analytics and AI technologies.
Accelerating Scientific Discovery
With AI, automation, robots, and other advanced technologies, Intel empowers lab leaders to accelerate discovery and improve accuracy.
Lab and Life Sciences Applications
Precision Medicine and Genomics
Accelerate tailored treatments built for each patient’s biological profile with powerful technologies that move, store, and process massive amounts of data.
AI-Enabled Digital Pathology
Enable faster results and rapid delivery into clinical workflows with AI-enabled digital pathology.
Modernizing Digital Infrastructure
Intel powers the modern digital infrastructure that helps researchers, clinicians, and other professionals accomplish more, reduce costs, and maintain the best outcomes—taking advantage of data-driven insights, from edge to cloud.
Panel PCs in Healthcare
Panel PCs provide a simple way for staff to review records, charts, and other information from anywhere medical staff need access to patient data.
Edge Analytics for Healthcare
Edge computing and analytics convert data into new insights to help improve patient outcomes while delivering financial and operational value.
Artificial Intelligence
Machine learning and AI offer extraordinary possibilities in healthcare and life sciences.
Kick-Start Your Healthcare and Life Sciences Technology Initiatives
Get your next project up and running quickly.
Developers
Accelerate your path to production with our tools and resources.
Industry Customers
Find solutions that help improve patient experiences and deliver fast, accurate results.
Partners
Supercharge your growth and deliver customer value in health and life sciences.
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Product and Performance Information
Yang, Shanling, et al. “Performance and Reading Time of Automated Breast US with or without Computer-Aided Detection.” Radiology 292, No. 3 (June 18, 2019): https://pubs.rsna.org/doi/10.1148/radiol.2019181816.
Jiang, Yulei, et al. “Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women with Dense Breast Tissue.” American Journal of Roentgenology 211, No. 2 (August 2018): 452–461. https://www.ajronline.org/doi/10.2214/AJR.18.19516.
“TGen Unravels Genetic Mystery of Disease,” Intel, n.d. Accessed March 14, 2022. https://www.intel.com/content/www/us/en/customer-spotlight/stories/tgen-customer-story.html.
KFBIO cervical cancer screening OpenVINO model throughput performance on Intel® Xeon® Gold 6148 processor:
NEW:
Test 1: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d); CentOS Linux release 7.5.1804 (Core); deep learning framework: Keras 2.2.4 and Intel-optimized TensorFlow: 1.13.1; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=8, both synthetic data and customer data; one instance/two socket; datatype: FP32.
Test 2: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); Intel® software: OpenVINO R2019.1.1094; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=1, eight asynchronous requests, both synthetic data and customer data; one instance/two socket; datatype: FP32.
BASELINE: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); deep learning framework: Keras 2.2.4 and Vanilla TensorFlow: 1.5; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=8, both synthetic data and customer data; one instance/two socket; datatype: FP32.
Performance claim based on internal Samsung testing as of March 2021. System configuration: Intel® Core™ i3-8100H CPU @ 3.0 GHz, 8 GB memory; OS: 64-bit Windows 10.