Zebra Medical Vision

Provide humanity with automated, accurate & timely medical image diagnosis

With an aging global population, and over 1 billion people joining the middle class and consuming healthcare services – the demand for medical imaging services has been steadily outpacing the supply of radiologists. We are on an unsustainable course, and everyone – patients, physicians, healthcare providers and society as a whole stands to suffer. We believe that by teaching computers to read and diagnose medical images, we can provide a solution to this global problem. Our algorithms assist radiologists in detecting often overlooked indications, and our products help providers analyze millions of imaging records to understand the risk profile of their patients, detect and predict disease and assist in building and managing preventative care programs. The company is led by experienced entrepreneurs, healthcare IT and medical imaging veterans, and works with some of the most forward thinking partners in the areas of patient care, big data and its uses in medical imaging. DEEP LEARNING Deep learning is an algorithmic technique that is revolutionizing what is possible in areas such as finance, communication, automotive, natural language processing, computer vision and more. It allows computers to analyze vast amounts of data and automatically detect patterns and features, or make predictions regarding certain conditions. To realize our vision of teaching computers to read medical images, we are combining our vast imaging database with deep learning techniques to build algorithms that will automatically detect and diagnose medical conditions – helping hundreds of millions of people receive fast, accurate imaging diagnosis. DATA DRIVEN CARE Zebra has accumulated one of the largest anonymized databases of medical imaging and clinical data available. The foundation of machine learning and deep learning is large quantities of high quality data, and we work with millions of imaging and correlated clinical and outcomes records to create high-performance algorithms faster, for numerous findings in parallel. Over the next few years we expect to release dozens of automated findings and insights to help improve patient care, at lower costs.

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