It’s one of the most frequently discussed questions in radiology today: What kind of long-term impact will artificial intelligence (AI) have on radiologists?

“Today there are 34,000 radiologists in the United States. Unless radiologists do things other than interpret imaging studies, there will be need for far fewer of them,” wrote Robert Schier, MD, with Radnet, in the Journal of the American College of Radiology.

EnvoyAI today announced a new integration with Insignia Medical Systems’ InSight PACS to provide artificial intelligence (AI)-enhanced imaging workflows to customers.

Bibb Allen Jr., MD, chief medical officer with the American College of Radiology (ACR)’s Data Science Institute (DSI) talked with HealthImaging about focusing Medical Image Computing and Computer Assistance Intervention's (MICCAI) artificial intelligence (AI) challenges on radiologists' clinical needs.

A new artificial intelligence (AI) tool lets scientists visualize a three-dimensional (3D) model of living human cells, even when the entire cell is not visible, according to a recent NPR story.

Using genomic sequencing to identify mutations in emerging strains of bacteria responsible for or at-risk of causing an outbreak is time consuming and labor-intensive. However, researchers have recently developed a new machine learning tool that can speed up the process.

MRI, for all it can do, has many design limitations—but an NYU School of Medicine team recently created an MRI device that users can slip on like a glove to produce high quality images of moving joints.

A team from the Wellcome Sanger Institute in the United Kingdom, the University of Otago in New Zealand and the Helmholtz Institute for RNA-based Infection Research in Germany has developed a machine learning tool capable of detecting strains of salmonella before they cause bloodstream infections. Findings were published May 8 in PLOS Genetics.