As researchers continue to make significant advances with artificial intelligence (AI) and deep learning, has the time come for radiologists to be concerned about their jobs?

Researchers have determined that the widespread use of genetic testing for newly diagnosed patients could lead to approximately $50 million in savings for the first year of breast cancer care in the United States.

As burnout continues to be a problem in radiology, what can be done to prevent it from getting even worse? According to a new commentary published in Academic Radiology, it’s time for the specialty to approach this issue from a new, more positive point of view.

Artificial intelligence-based computer-aided detection (AI-CAD) software can help radiologists detect more cancers when interpreting mammograms, according to a new study published in the Journal of Digital Imaging.

Higher levels of body fat are linked to changes in the brain, including differences in white matter and reductions in gray matter volume, according to new findings published in Radiology.

Researchers have developed a new technique, DeepPET, that uses deep learning to turn PET imaging data into high-quality images at a much faster rate than traditional methods. 

Arizona-based Banner Health and Radiology Partners announced Tuesday, April 23, that they have signed an agreement to provide imaging services in the metro Phoenix area. The new enterprise will be known as Banner Imaging.

The high sensitivity associated with breast MRI makes it an effective tool for detecting breast cancer, but the costs and long acquisition times have kept it from being embraced as a supplemental screening option.

Deep learning techniques can be used to detect catheters and tubes in pediatric x-rays, according to a new study published in the Journal of Digital Imaging. These findings could lead to advancements that prioritize x-rays with poorly placed catheters, bringing them to a specialist’s immediate attention.