The NYU School of Medicine’s department of radiology and Facebook recently announced a new collaborative research project focused on using artificial intelligence (AI) to make MRI scans up to 10 times faster.

A small group of experienced radiologists, connected by machine learning algorithms that enable them to work together as a “hive mind,” can achieve higher diagnostic accuracy than individual radiologists or machine learning algorithms alone, according to new research presented on Sept. 10 at the Society for Medical Imaging Informatics in Medicine (SIIM)’s Machine Intelligence in Medical Imaging conference.

A deep learning system (DLS) designed to assess liver fibrosis using CT images was found to be highly accurate, according to a new study published in Radiology.

The creators of a fully automated medical image analysis program to detect breast tumors was inspired by the classic video game Tetris. It is also almost twice as fast at finding lesions as existing techniques and just as accurate.

Researchers at the University of Central Florida’s Center for Research in Computer Vision have created an artificial intelligence (AI) algorithm that can detect specks of lung cancer in CT scans with 95 percent accuracy.

Fujifilm Corporation and the Indiana University School of Medicine in Indianapolis have announced a new research agreement that will focus on applying artificial intelligence (AI) to medical imaging diagnostic support systems.

The Radiological Society of North America (RSNA) announced the launch of its second annual machine learning challenge on Monday, Aug. 27. Teams will be invited to develop algorithms to identify and localize pneumonia in chest x-rays.

A deep convolutional neural network (DCNN) can be trained to analyze MRI scans and differentiate between tuberculous spondylitis and pyogenic spondylitis, according to a new study published in Scientific Reports.

Toronto-based Exact Imaging and Cambridge, U.K.-based Cambridge Consultants have announced a new international partnership focused on improving the way specialists visualize and detect prostate cancer.