In 2009, the U.S. Preventative Services Task Force (USPSTF) revised its breast cancer screening guidelines. These changes may be one reason for a continuous decline in breast screening exams in the U.S. since 2010, according to results of a study published by the American Journal of Roentgenology.
Obese patients in the U.K. are getting too big to fit into MRI scanners, which can lead to health complications after canceled exams, according to a recent article by BBC News.
Penn State College of Medicine announced, April 2, the creation of a new department of radiation oncology, naming Rickhesvar Mahraj, professor of radiology and pediatrics, as its interim chair.
A team of scientists found the percentages of cancer survivors unable to afford medication or incapable of receiving care dropped each year from 2010 to 2016—during the implementation of the Affordable Care Act (ACA).
GE Healthcare has announced it will sell its revenue cycle, ambulatory care and workforce management software units to private equity firm Veritas Capital for $1.05 billion, following earlier plans for General Electric to sell assets and rumors the entire company could be broken up.
Nvidia has released a new series of products aimed to accelerate the research and development of artificial intelligence (AI), with one focused on medical imaging, according to a recent article by Forbes.
Photo courtesy of the Society of Nuclear Medicine and Molecular Imaging.
A new positron emission tomography (PET) imaging agent could help guide and evaluate treatments for people with neurological diseases such as Alzheimer's and multiple sclerosis, according to a release from the Society of Nuclear Medicine and Molecular Imaging.
Two Minnesota high school students sat down with imaging specialists to learn about the importance of the job in medicine and how to gain valuable work experience in the position.
New artificial intelligence research from Google, presented at MIT Technology Review's EmTech Digital 2018 conference in San Francisco, may point to reducing the number of radiologist-annotated images required to train a deep learning algorithm for medical imaging applications.