Artificial Intelligence

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.

Radiologists outperformed a convolutional neural network (CNN) and radiomic analysis (RA) at classifying contrast-enhancing lesions on multiparametric breast MRI, according to a Nov. 13 study published in Radiology. With more training, however, CNNs may soon close that gap.

The study will determine whether CMAs can obtain echocardiograms that, when reviewed by cardiologists, will detect more patients with cardiac disease compared to a standard physical examination with an electrocardiogram (ECG) in a primary care setting, according to a Northwestern University press release.

A decision support tool can help physicians better diagnose bladder cancer treatment response on CT, according to an Nov. 10 study published in Academic Radiology.

Deep learning can estimate full-dose PET images from scans with significantly lower dosages, according to a new study in the Journal of Digital Imaging. The method may make performing PET scans safer and more affordable.

The FDA recently administered 510(k) clearance to software developed by MaxQ AI that uses AI to detect brain bleeds on CT images, according to a report published Nov. 8 by AI in Healthcare.