The society recently announced this “unprecedented collaboration,” which was made possible by the help of 60 physician volunteers and a fellow imaging interest group.
Using brain MRI and a deep learning network, researchers have achieved 97% accuracy at classifying a gene mutation indicative of growth in localized gliomas.
A deep-learning algorithm has accurately measured 26 pairs of uneven leg lengths on children’s x-rays at a rate 96 times faster than that recorded by an experienced, subspecialty-trained pediatric radiologist using manual means.
“By improving identification of patients needing osteoporosis treatment or prevention, XRAIT may help reduce the risk of a second fracture and the overall burden of illness and death from osteoporosis,” epidemiology experts said Tuesday.
Imaging experts are concerned artificial intelligence is not being trained on robust datasets, while developers say their platforms are already working in real-world situations.
“This is one of the first times that artificial intelligence has been used to better define the different parts of a newborn's brain on an MRI," Canadian researchers explained March 26 in Frontiers in Neuroscience.
An increasing number of artificial intelligence firms are tweaking existing platforms or creating new models to help clinicians handle the growing pandemic.
“With AI, cancer imaging can move from an inherently subjective tool to a quantitative and objective asset for precision medicine approaches," Columbia University Irving Medical Center radiologists wrote Friday.
The college shared high-level principles in its comments submitted to the White House Office of Management and Budget, emphasizing validation, transparency and safety.
“These findings suggest that our algorithm approaches the upper bound of possible performance of an experienced radiologist,” experts wrote Wednesday in Radiology: Artificial Intelligence.
