AIRS Medical showcases award-winning MRI enhancement AI solution at RSNA 2022

SEOUL, South Korea, Nov. 14, 2022 /PRNewswire/ — AIRS Medical, Inc., a leading medical AI solution provider, announced its participation in the 108th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA 2022), the world’s leading annual imaging forum at McCormick Place in Chicago held from November 27 to 30th.

At RSNA booth #4744, AIRS Medical showcases SwiftMR™, an FDA 510(k)-cleared AI Imaging solution that enhances MR images acquired under various conditions, contributing to better image quality and patient experience. SwiftMR™ utilizes conventional MR imaging techniques such as parallel Imaging and compressed sensing, combined with its award-winning deep learning technology. SwiftMR™ thus enhances SNR and sharpness of the images, allowing radiologists to read with confidence and ease.

Along with the booth demonstration, there will be a special presentation from Roh-Eul Yoo, MD, Ph.D. from Seoul National University Hospital. Dr. Yoo will be sharing her experience working with SwiftMR in various clinical scenarios, along with the results of recent research collaborations proving the power of SwiftMR in providing superior image quality. The presentation will start at 13:30 pm, 28th of November at AI Theater, South Hall A, Level 3.

SwiftMR™ was cleared by both the US FDA and the Korea Ministry of Food and Drug Safety (MFDS) in 2021. Since its official commercial launch in Korea in the fourth quarter of 2021, SwiftMR™ has been installed in more than 100 hospitals, for an average of 30,000 monthly MRI exams and a grand total of more than 280,000 MRI exams. After its successful launch and with proven performance in Korea, the company is rapidly expanding globally.

About AIRS Medical

AIRS Medical Inc., founded in Oct 2018, is a medical AI company based in Seoul, Korea, that develops innovative products and services to improve patient experience. AIRS Medical helps healthcare systems achieve greater institutional efficiency in areas where a lack of productivity limits clinical value.

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