Artificial Intelligence

The supply of radiation oncologists hasn’t kept up with the global demand for radiation therapy. But could experts from across the world help create an AI algorithm capable of closing that gap?

Deep learning techniques can be used to detect catheters and tubes in pediatric x-rays, according to a new study published in the Journal of Digital Imaging. These findings could lead to advancements that prioritize x-rays with poorly placed catheters, bringing them to a specialist’s immediate attention.

A detailed roadmap outlining research priorities for artificial intelligence (AI) in radiology was published April 16 in Radiology, and the organizations involved have announced that a second report is due later this year.   

The human-level success of deep learning has made some in medicine question whether automation may eventually take-over many tasks performed by radiologists. An author, and radiologist, put that question to bed in an April 18 editorial published in the Journal of the American College of Radiology.

Aidoc, a Tel-Aviv, Israel-based medical imaging company, announced that it has raised $27 million in a Series B investment round led by Square Peg Capital.

“Our goal was to provide a blueprint for professional societies, funding agencies, research labs, and everyone else working in the field to accelerate research toward AI innovations that benefit patients," wrote lead author, Curtis P. Langlotz, MD, PhD.

Representatives from numerous healthcare organizations gathered in 2018 and developed a new document focused on helping researchers use AI algorithms in radiology to improve patient care. 

A recent NPR report traced the development and approval of the fist AI software approved to diagnose diabetic retinopathy and examined challenges the administration may face as more software makers look to enter the market.

Pairing the established denoising algorithm NeighShrink with chi-square unbiased risk estimation (CURE) was superior to conventional methods at reducing noise in MR images, reported researchers of a study published in Artificial Intelligence in Medicine.

Using computer-aided detection (CAD) software powered by artificial intelligence leads to fewer false-positive mammograms, according to new findings published by the Journal of Digital Imaging. Significant cost savings could also be realized by making such a switch.

The American College of Radiology (ACR) Data Science Institute (DSI) just launched its ACR AI-LAB software platform and has already secured a key collaborator: GE Healthcare.

The American College of Radiology (ACR) Data Science Institute (DSI) has launched the ACR AI-LAB, a free software platform that will help radiologists collaborate to create, validate and use AI. The college also announced it is partnering with GE Healthcare on the ACR AI-LAB.