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Bridges Clinical Data Sources and Healthcare AI Developers to Accelerate Innovation and Deployment.
FREMONT, CA: maiData Corporation, the provider of Big Data for medical AI developers, announced it had joined the nVidia Inception Program, a virtual accelerator program that is designed to nurture startups during critical stages of product development, prototyping, and deployment, to revolutionize industries with advancements in artificial intelligence (AI) and data science.
maiData was founded to solve the persistent shortage of clinical data available to medical algorithm developers. The company’s unique solution will reduce the cost and burden of data collection for AI companies and shorten AI algorithms time-to-market. This means that clinicians get clinical decision support more quickly, which can result in better patient care.
“AI developers need a large and diverse spectrum of cases to ensure the generalizability of their algorithms, and to avoid bias. It is unlikely that just getting data from your own institution, no matter how large, or even from a handful of collaborators, will be good enough. Developers must do more to ensure that algorithms will work as expected when used clinically at all facilities, and having the largest spectrum of cases possible is a necessary step towards that goal,” said Robert Nishikawa, Ph.D., Professor of Radiology at the University of Pittsburgh.
maiData has developed a streamlined approach to data collection that will significantly reduce the challenges associated with traditional curating data methods for the development and testing of medical AI algorithms. Serving as the bridge between clinical data sources and medical AI developers, maiData accelerates AI innovation and deployment.
“AI companies have traditionally created their own relationships with individual clinical facilities, which is time consuming and adds unpredictability in cost, effort and quality that can result in development delays,” said Julian Marshall, CEO of maiData. “maiData can help AI companies streamline case collection efforts and provide seamless delivery of large volumes of medical images and metadata for developing robust AI algorithms.”