Medical Imaging

Google was hoping to release a massive dataset of chest x-rays to the public in 2017, but had to cancel at the last minute after receiving an urgent call from the National Institutes of Health (NIH).   

Ultromics, a U.K.-based healthcare technology company, has gained FDA clearance for its new AI-powered image analysis solution.

RSNA 2019, the world’s largest radiology conference, kicks off at Chicago’s McCormick Place on Sunday, Dec. 1. This year's show promises to include more AI content than ever before.

Hologic has received FDA approval of its AI-based solution designed to speed up interpretation times for digital breast tomosynthesis (DBT) exams.

Deep learning techniques have shown potential to change cardiac MRI forever, according to a new analysis published in the American Journal of Roentgenology. However, the authors wrote, it is also important to remember deep learning’s current limitations.

RadNet has announced a new partnership with Santa Clara, California-based Whiterabbit.ai to improve mammography screening rates and breast cancer care through the use of AI and other advanced technologies.

Radiologists are in a position to demonstrate their value and lead the implementation of AI in healthcare—but keeping up with these evolving technologies is easier said than done.

Healthcare technology is constantly changing, something radiologists know all too well. And while some within the specialty have expressed fear or concern over the continued rise of AI, a new commentary in Clinical Radiology noted that it’s all par for the course—and radiologists must rise to the occasion yet again.

XACT Robotics, a radiology technology company with offices in Hingham, Massachusetts, and Israel, received FDA clearance for the use of its hands-free robotic system during CT examinations.

Fifty-three percent of physicians say they are optimistic about AI’s potential effect on healthcare, according to a new survey of more than 1,700 physicians published by the Doctors Company.

Facebook and the NYU School of Medicine made headlines back in August 2018 when they announced their plan to improve MRI times using AI.

Deep convolutional neural networks (CNNs) can be trained to predict sequence types for brain MR images, according to new research published in the Journal of Digital Imaging.