Deep learning and artificial intelligence (AI) are often associated with identifying nodules and classifying images, but a recent study found convolutional neural networks (CNNs) can be utilized in radiology workflows to determine musculoskeletal MRI protocols.
Ohio State University researchers have developed an artificial intelligence (AI) algorithm able to analyze a single brain CT scan in just six seconds, according to an article published online March 28 by the Lantern.
Eight members of the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning describe a radiologist-friendly overview examining past, present and future applications and how the field might benefit from embracing deep learning.
New artificial intelligence research from Google, presented at MIT Technology Review's EmTech Digital 2018 conference in San Francisco, may point to reducing the number of radiologist-annotated images required to train a deep learning algorithm for medical imaging applications.
A group of U.S. researchers created a natural language processing (NLP) system which outperformed traditional rule-based methods in identifying lumbar spine findings, according to a study published online in Academic Radiology.
As deep learning in medical imaging continues to advance, two leading experts argue in an editorial in the Harvard Business Review that it will only result in positive impacts on the field—rather than replace imaging professionals with computers.
Researchers from Massachusetts General Hospital (MGH) have developed a machine learning and artificial intelligence (AI)-based technique that may generate higher quality images without having to collect additional data.