Good technology is designed to let providers focus on the patient and their care. At Intel, our goal is to build technology that enriches the life of every person on earth. Technologies like artificial intelligence (AI), robotics, and the Internet of Things (IoT) are making healthcare and life sciences more connected, personalized, and intelligent.
For example, AI in medical imaging has enabled providers to identify anomalies more quickly and accurately, which can lead to faster diagnoses.1 Other applications of AI in healthcare support customized patient care, surgical precision, intelligent healthcare analytics, and new genomics research. Combined with IoT healthcare technologies, AI has transformed telemedicine, patient monitoring, and electronic health record (EHR) keeping.
Intel healthcare technologies create efficiencies that enable providers to focus more on the human side of care delivery. In lab and research environments, our technology innovations give researchers powerful tools to make breakthrough discoveries and solve some of the world’s largest healthcare and life science challenges. By working together with solution providers and end users in the healthcare community, we’ll continue to develop transformative technologies for the future of healthcare and life sciences.
Every second matters when diagnosing critical conditions. GE Healthcare and Intel collaborated on a medical imaging solution that applies AI to chest X-rays to detect life-threatening conditions in seconds and better prioritize critical cases.1
A robot prototype developed by the startup Akara and powered by an Intel® Movidius™ VPU is disinfecting contaminated surfaces using UV light. Akara’s goal is to help hospitals sanitize rooms and equipment, aiding in the fight against COVID-19.
Intel and the Broad Institute collaborate on data center solutions for genomics analytics. This effort is making it possible to combine research data sets worldwide so that researchers can access data that may have been unavailable to them before.
Medical Informatics Corp. (MIC) offers a remote intensive care monitoring solution for hospitals to rapidly expand ICU capacity. Run on Intel® Xeon® processors, Sickbay™ helps hospitals protect staff, serve more patients, and deliver remote access to comprehensive data.
Intel delivers a range of compute for CT, MRI, PET, X-ray, ultrasound, and endoscopy systems to help clinicians perform accurate, real-time diagnostics. In addition, AI technologies in medical imaging can help improve patient experiences, reduce clinician workloads, and increase operational efficiency.
Turn data into insights quickly. Intel provides a foundation for the latest big data platforms, AI models, edge computing, and other analytics in health information technology. At the edge or in the cloud, analytics can help predict when and where care will be needed, track patient vitals in real time, and much more.
Lab breakthroughs lead to new strategies in healthcare and medicine, including customized treatments based on a patient’s own genetic data. From genomics to drug discovery to infectious disease management, Intel® technologies help digitize the lab, making data analytics and research efficient and measurement consistent.
Telemedicine and telehealth span a range of techniques for remote care, including vitals monitoring, chronic disease management, and specialist consultations. Intel® technologies enable telehealth solutions that deliver comprehensive, integrated patient care.
New categories of robots powered by Intel® technologies are transforming healthcare. Surgeons can perform noninvasive surgeries using AI to better see inside their patients. Disinfection and logistics robots help with manual tasks such as cleaning and delivering supplies. Patients can get support from assistive robots in their homes.
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GE Healthcare medical imaging case study: System 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 Intel® Distribution of OpenVINO™ toolkit optimized TF model resulting in a total inferencing time of 0.913 seconds.