As AI technologies continue to evolve, they may be able to make a significant impact on patient care by reducing the amount of time physicians spend sorting through paperwork and documentation.
Machine learning (ML) can help providers extract all relevant facts from radiology reports in real time, according to a new study published in the Journal of Digital Imaging.
Researchers have developed an algorithm that predicts breast malignancy using a patient's imaging results and detailed health records, sharing their findings in Radiology.
Canon Medical Systems USA announced Tuesday, July 18, that its new deep convolutional neural network (DCNN) image reconstruction solution for CT scans has received FDA approval.
AI marketplaces could have a significant impact on the future of radiology, according to a new analysis published by the Harvard Business Review.
Zebra Medical Vision announced Monday, June 17, that its AI solution for intracranial hemorrhage (ICH) alerts has gained FDA approval.
Researchers have used AI to gain additional insight from the brain MRI scans of multiple sclerosis (MS) patients, sharing their findings in Nature Digital Medicine.
Artificial neural networks (ANNs) can help radiologists classify ground-glass opacities (GGO) with improved accuracy, according to new findings published in Clinical Radiology.
Augmented datasets can improve the overall accuracy of deep convolutional neural networks (DCNNs), according to new findings published in Clinical Radiology.
Businessman Vinod Khosla, co-founder of Sun Microsystems and a longtime venture capitalist, said this week that he thinks any radiologist still practicing in 10 years “will be killing patients every day.”
Machine learning can help improve the overall performance of CT scans, reducing radiation exposure and boosting image quality, according to new findings published in Nature Machine Intelligence.
Machine learning could be a real game-changer for interventional radiology (IR) in the years ahead, according to a new analysis published in the American Journal of Roentgenology.