High-Quality Medical Images Help Assess Players’ Health

Wayne, N.J., February 19, 2019 – FUJIFILM Medical Systems U.S.A., Inc. , a leading provider of endoscopic imaging solutions, today announced a three-year, sole-source relationship with Gastrologix, the nation’s only group purchasing organization (GPO) working solely with independent gastroenterology (GI) physicians. 

Comprehensive image and information management solution features new pediatric innovative workflow and integration with Philips Forcare for enhanced interoperability across health systems and hospitals. 

MARLBOROUGH, Mass., February 11, 2019 -- Hologic, Inc. (Nasdaq: HOLX) announced today the U.S. launch of Unifi™ Analytics, a business intelligence tool that allows healthcare facilities to manage their mammography devices, monitor technologist performance, and prevent unanticipated downtime via predictive tube replacement technology. 

It’s all about the data. We’ve been saying this for years. We can choose to look at this in one of two ways. It’s either a constant truism or it actually evolves and gains mass over time. In the age of artificial intelligence, it is both. 

Artificial and augmented intelligence are driving the future of medical imaging. Tectonic is the only way to describe the trend. And medical imaging is at the right place at the right time. Imaging stands to get better, stronger, faster and more efficient thanks to artificial intelligence, including machine learning, deep learning, convolutional neural networks and natural language processing. So why is medical imaging ripe for AI? Check out the opportunities and hear what experts have to say—and see what you should be doing now if you haven’t already started.

Not just for years but for decades, the department of radiology at the University of Wisconsin School of Medicine and Public Health in Madison has been leading the charge on creating innovative technology and translating imaging research into clinical practice.

Countless predictions have been made about artificial intelligence and machine learning changing imaging screening and diagnosis at the point of patient care—and clinical studies and experience are now proving it. Radiologists say the impact is real in improving diagnosis of cancers and quality of care, consistency among readers and reducing read times and unnecessary biopsies. One shining example targets the evaluation of breast ultrasound imaging.