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VUNO Inks Deal with Samsung Electronics to Embed AI-powered Algorithms in Samsung's Premium Mobile X-ray System

VUNO's road-tested, clinically-proven chest X-ray detection software is reshaping the delivery of medical imaging diagnostics by being fully integrated with Samsung Electronics' X-ray system


News provided by

VUNO Inc.

Jun 30, 2021, 09:00 ET

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SEOUL, South Korea, June 30, 2021 /PRNewswire/ -- South Korean artificial intelligence (AI) developer, VUNO Inc., announced today a partnership with Samsung Electronics Co., Ltd., global medical device company to incorporate its AI-powered chest X-ray diagnostic solution, VUNO Med®-Chest X-ray™, into Samsung's premium mobile digital X-ray system, GM85. The AI integrated X-ray suite is slated to debut in Korea and other major markets around the globe later this year. This is one of the latest extensions of VUNO's ambition to leverage the full potential of AI to better orchestrate clinical workflows by empowering medical service providers to deliver patient-focused care.

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VUNO Med®-Chest X-ray™ & GM85
VUNO Med®-Chest X-ray™ & GM85

VUNO's AI algorithm has been successfully validated for its improved clinical utility and efficiency in academic journals and clinical settings. Such a proven track record of success along with its lightweight enabled integration capabilities is the key factor that has contributed to making this deal happen.

Samsung's GM85, a premium mobile digital radiography system with a lightweight and ultra-compact design, combines a broad range of advanced technology including a quick charging and long-lasting battery to provide enhanced user-convenience and superior image quality. Featuring VUNO's AI, the integrated mobile X-ray suite will be able to instantly deliver AI results on the spot upon scanning an image while the patient is still in the hospital. This will serve as a useful diagnostic support tool in the emergency room and intensive care units where real time analysis is critical and under other medical environments with limited or no network connections.

"I am delighted to partner with Samsung Electronics in embedding our best-of-breed AI solution into their mobile digital X-ray devices. This collaboration will bring us closer to making our market-ready AI applications more accessible across the globe. We will continue to focus on advancing our technology to deliver improved patient outcomes" said Hyun-jun Kim, co-founder, and CEO of VUNO.

"Incorporating VUNO's AI technology, we can introduce a more sophisticated mobile X-ray system with AI enabled CAD," said Woo-young Jang, Samsung Electronics' Head of DR Business Team. "We will continue to extend our partnerships to develop a leadership position in the global X-ray market".

VUNO Med®-Chest X-ray™ accurately and instantly detects and flags suspected chest abnormalities indicative of major pulmonary diseases such as tuberculosis, pneumonia, lung cancer based on five of the most common thoracic findings such as nodule/mass, pneumothorax, interstitial opacity, pleural effusion, and consolidation. This solution has been in full commercial deployment in Korea and Europe after obtaining MFDS (Ministry of Food and Drug Safety) approval and CE mark in August 2019 and June 2020 respectively. 

About VUNO Inc.

VUNO is a Seoul-based leading AI medical software company that applies deep learning to develop data-driven AI medical solutions using medical imaging, pathology, biosignal and medical speech data. We strive to present a whole new level of experience to medical practitioners in their day-to-day workflows, empowering them to make better diagnostic decisions faster and more accurately and to provide quality patient care and treatment planning to patients. For more information, visit www.vuno.co. 

[1] Jinkyeong Sung, MD et al., Added Value of Deep Learning–based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study, Radiology. 2021 Mar 23;202818. Available at: https://pubs.rsna.org/doi/abs/10.1148/radiol.2021202818?journalCode=radiology

SOURCE VUNO Inc.

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