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