Artificial Intelligence

“Our research demonstrates that deep-learning models integrating routine imaging scans obtained at multiple time points can improve predictions of survival and cancer-specific outcomes for lung cancer," wrote Hugo Aerts, PhD, in a recent study published in Clinical Cancer Research.

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?

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.

“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.

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.

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.

Machine learning has the potential to reshape the patient-doctor relationship, according to a new review published in the New England Journal of Medicine, but it must overcome a few challenges first.

A new AI model can accurately determine a patient’s five-year cancer risk based on a single breast MR image, outperforming state-of-the art risk assessment models.

An AI platform designed to quickly read an x-ray and determine the manufacturer and model of a cardiac rhythm device may quicken treatment in the event of device failure.

The algorithm was externally validated on 486 normal chest radiographs and 529 abnormal chest radiographs taken from four different institutions across multiple continents.

The FDA announced Tuesday, April 3, that it is working on a new framework to regulate AI-based medical devices that continually learn from healthcare data.