Medical Imaging

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 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.

Discussions about AI and radiology often focus on the researchers who help develop the algorithms and radiologists themselves. But a new analysis published in Academic Radiology shines a light on another key role in the implementation of AI: the imaging informaticist.

Deep learning neural networks can improve the detection of tuberculosis (TB) and provide health systems with considerable cost savings, according to new findings published in Scientific Reports.

AI can detect brain hemorrhages in CT scans more accurately than some radiologists, according to new findings published by Proceedings of the National Academy of Sciences.

Deep learning algorithms can be trained to flag suspicious chest x-rays in an emergency department (ED) setting, according to new research published in Radiology.