Launch of AI Incubator to Stimulate Innovation and Drive Adoption
By MedImaging International staff writers
Posted on 18 Dec 2018
MEDNAX, Inc, and MEDNAX Radiology Solutions (Sunrise, FL, USA) has launched the MEDNAX Radiology Solutions Artificial Intelligence (MDR-AI) Incubator, which brings together radiologists, a rich clinical dataset, and a select group of technology partners to stimulate innovation and product development in radiology, thereby improving radiologist’s accuracy and efficiency and quality of patient care.Posted on 18 Dec 2018
Currently, 15 active partners at various levels of engagement are delivering value through production use of the existing models within the MEDNAX Radiology Common Imaging Platform. Together with its growing network of partnerships, MEDNAX Radiology Solutions has built an ecosystem made up of the largest and most diverse data set, with AI-based natural language processing, access to the leading practice of radiologists providing data curation services, and an ability to validate models on a national scale.
“The MDR-AI Incubator brings together a range of companies, from start-ups to blue-chip technology leaders and innovators, to develop innovative tools in a collaborative, safe and secure environment,” said Imad Nijim, Chief Information Officer of MEDNAX Radiology Solutions and Virtual Radiologic (vRad). “Our approach to AI has always been to develop sound models that have immediate and measurable impact. It will allow medical and technical leaders to build tools that improve the practice of radiology for physicians and their patients. The goal is to deliver something that pushes our entire industry forward.”
“MEDNAX Radiology Solutions and vRad have worked to leverage deep learning in a real-time practice environment,” said Ricardo Cury, M.D, Chief Medical Officer of MEDNAX Radiology Solutions. “This work demonstrates how the right clinical and technical collaboration can empower radiologists, increase their time as doctors and diagnosticians, and ultimately improve patient outcomes. The combination of deep learning technology with large clinical datasets and expertise serves as a model of how cutting-edge technology can develop tools that complement, not supplant, clinicians and improve care. We are encouraged by the manner in which AI can improve how radiologists operate and quickly deliver high-quality, accurate diagnoses to referring physicians.”
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