If validated further, the algorithm could be used to flag urgent scans in radiology workflows, especially in resource-strapped regions.

With further testing and validation, Hyungjin Kim, with Seoul’s National University College of Medicine and colleagues believe radiologists may one day use the tool to individualize treatment and achieve better outcomes.

"Using this new tool may uncover the characteristics of different types of clots that were previously unrecognized by humans," researchers wrote in the journal eLife.

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.

Stanford University School of Medicine clinicians detailed their suggestions to spur the development of artificial intelligence Tuesday in Radiology.

Researchers from Spain used nearly 1,000 pelvic and lower limb MRIs, displaying various forms of the disease, to create their model.