The resource currently includes more than two dozen applications, with uses varying from triaging head CT imaging patients to analyzing wrist radiographs.
Researchers with several academic institutions recently made that discovery using dozens of submissions from the RSNA Pediatric Bone Age Machine Learning Challenge.
AI triage could prove to be pivotal elsewhere, however, by cutting the time radiologists spend analyzing cases and then prioritizing those that are most urgent, one expert noted.
Those include an AI offering from Oxford, England-based Ultromics, which automates cardiac analysis to help with early detection of cardiovascular disease.
That’s according to a new survey of healthcare stakeholders, highlighted in November’s European Journal of Radiology.
A deep learning software tool powered by artificial intelligence has been proven to boost clinicians’ ability to detect lung cancer on chest x-rays.
The investor-owned, Los Angeles-based provider announced Thursday, Nov. 7, that it’s teaming with Whiterabbit.ai to further spread AI tools across its 340 outpatient centers.
Presagen announced Oct. 30 the launch of its AI Open Projects platform, a tool that allows radiology practices worldwide to share images and help to build AI products that are “robust, scalable and unbiased.”
Case Western Reserve scientists have developed a tool that may help predict whether precancerous breast lumps will worsen, heading off the need for unnecessary radiation treatment.
Researchers found that deep convolutional neural networks (CNNs) can predict sequence types for brain MR images, sharing their findings in the Journal of Digital Imaging.
Mammography does a good job detecting calcifications, but its specificity for distinguishing benign from malignant findings remains low.
A deep-learning algorithm can be as effective—or more effective—than radiologists in finding intercranial hemorrhages on CT scans.