Using a variation of an unconventional imaging approach called “ghost imaging," an international team of researchers has successfully taken low dose three-dimensional (3D) x-ray images which may help make screening for early signs of diseases and cancer cheaper, safer and more accessible.

Tech company Amazon has launched a new medical language processing service that, by using artificial intelligence (AI), can extract data from patient records and reports to help healthcare professionals make better treatment decisions, address data privacy and decrease overall costs, according to a report published Nov. 28 by TechCrunch.

Using fMRI, researchers found that brain regions associated with impulsivity were altered in men who are addicted to video games, according to new findings presented Nov. 28 at RSNA 2018.

A novel imaging technology—magnetoencephalography (MEG)—allows scientists to measure levels of iron-based minerals in the brain, which may provide insight into neurodegenerative disorders such as Alzheimer’s disease, reported researchers from Massachusetts General Hospital (MGH) in Boston.

Intel and GE Healthcare* are teaming up to deliver artificial intelligence (AI) solutions across multiple medical imaging formats to help prioritize and streamline patient care.

"Neural networks, such as cycle-consistent generative adversarial network (CycleGAN), are not only able to learn what breast cancer looks like, we have now shown that they can insert these learned characteristics into mammograms of healthy patients or remove cancerous lesions from the image and replace them with normal looking tissue,” said Anton S. Becker, MD, at RSNA 2018 in Chicago.

As time goes on, artificial intelligence (AI) is becoming more widely accepted as a necessary component of clinical workflow in medical imaging. According to Tarik K. Alkasab, MD, PhD, a radiologist at Massachusetts General Hospital in Boston, AI has the potential to make radiology reporting much more consistent and ultimately help radiologists make smarter decisions.

NYU School of Medicine’s Department of Radiology announced it will release more than 1.5 million anonymous MR images from its fastMRI collaboration with Facebook AI Research (FAIR), a partnership focused on using AI to speed up MRIs.