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Lunit Demonstrates Latest Updated AI Solutions for Chest and Breast Radiology at RSNA 2019

By MedImaging International staff writers
Posted on 05 Dec 2019
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Image: Lunit INSIGHT CXR (Photo courtesy of Lunit)
Image: Lunit INSIGHT CXR (Photo courtesy of Lunit)
Lunit (Seoul, South Korea), a medical artificial intelligence (AI) software company providing AI-powered total cancer care, returned to the 105th Radiological Society of North America (RSNA) this year with the latest, up-to-date AI solutions for chest and breast radiology.

At RSNA 2019, visitors had a hands-on experience with cutting-edge technology, including artificial intelligence (AI), 3D printing and virtual reality. The event allowed visitors to dive into the latest research, talk to over 700 vendors about their innovative products and services, enhance their skills at over 400 educational courses, and join 50,000 attendees to meet the leaders shaping tomorrow. At its RSNA 2019 booth, Lunit demonstrated its state-of-the-art software—Lunit INSIGHT CXR 3 and Lunit INSIGHT MMG—which have been tested on more than three million images from over 80 countries combined.

These software products, now installed and in use at hospitals and other medical institutions around Mexico, the UAE, China, and South Korea, have been receiving positive comments and feedback from users, especially for its outstanding accuracy at 97-99% in the detection of lung abnormalities and breast cancer. During RSNA 2019, Lunit presented key clinical study results conducted to validate the specific clinical utility of its products, along with other abstracts that study AI-driven mammography & DBT, and AI-based detection of chest abnormalities, such as pneumothorax and tuberculosis.

Additionally, Brandon Suh, CEO of Lunit, took to the stage to give a presentation on the company’s most recent activities and product developments. Titled, “AI-powered Precision Diagnostics - Beyond Expert Level Imaging Biomarkers for Chest and Breast Imaging,” Dr. Suh presented how Lunit thrives to expand the boundaries of AI-driven medical image analysis and how its AI solutions present a new paradigm of AI-assisted radiology workflow.

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