Artificial Intelligence

Artificial intelligence (AI) continues to change the way radiologists work. The major shift predicted by many isn’t happening as quickly as expected—but AI is reaching areas some didn’t anticipate.

Facebook's most recent data scandal had lawmakers grilling founder and CEO Mark Zuckerberg in a Senate hearing April 11 and presents bioethics lessons for healthcare leaders who are creating AI models for clinical decision making

W. Art Chaovalitwongse, PhD, from the University of Arkansas, discussed using radiomics versus deep learning-based features to predict clinical outcomes from medical imaging data.

Primary care physicians may now be able to identify moderate to severe levels of retinopathy in adult patients with diabetes using a recently FDA-approved artificial intelligence (AI) imaging device.

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.

A team of Stony Brook University-led researchers in New York created a method using deep learning digital pathology to map cancerous immune cell patters that may help guide new cancer therapies.

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.

Jensen Huang, CEO of Nvidia, a California-based technology company, recently revealed plans to construct a medical imaging supercomputer affectionately named Clara.

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.