Artificial Intelligence

Artificial intelligence (AI) is sure to change radiology, but a recent feature in IEEE Spectrum argues its true effect on medicine will be felt not in imaging, but in the pathology lab.

A three-dimensional (3D) deep residual network accurately detected and classified cerebral microbleeds (CMBs) on susceptibility-weighted magnetic resonance images (SWIs), reported a team of San Francisco-based researchers.

Sunrise, Florida-based medical group Mednax announced it is launching an artificial intelligence (AI) incubator focused on creating innovative radiology solutions.

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.

"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.

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.

Using artificial intelligence (AI), researchers from Stanford University in California have reduced the amount of gadolinium left behind in a patient’s body after an MRI exam, according to research presented at RSNA 2018 in Chicago

Over the past year, 2018 RSNA President Vijay Rao, MD, has heard radiologists across the globe express their “hype, hope and fear” of the sudden rise in technology. During her presidential address at RSNA's 2018 Annual Meeting in Chicago she put those fears to bed, while placing the onus on radiologists to help do the same.

A deep learning algorithm showed capability in screening chest x-rays for diseases similar to the interpretations of trained radiologists, but did so in a matter of seconds, according to Stanford University researchers.

Breast radiologists had slightly higher diagnostic performances when using artificial intelligence (AI) with no additional reading time required, according to a study published Nov. 20 in Radiology.

A model based on radiomic features extracted from CT scans can help predict which ground glass nodule (GGN) cases require surgery and may reduce overtreatment, according to researchers at the Affiliated Suzhou Hospital of Nanjing Medical University in Suzhou, China.

A machine learning algorithm based on perfusion-weighted MRI accurately differentiated between benign and malignant tumors in the uterus, according to researchers at Tehran University of Medical Sciences (TUMS) in Iran.