This deep neural network was able to extract vast amounts of data from images and deduce a higher cancer risk association when compared to even the best mammographic breast density model.
Artificial intelligence may help to inject more humanity back into the medical profession, despite concerns that it may make medicine colder and more sterile.
The federal agency responsible for clearing new artificial intelligence algorithms wants to better understand the pitfalls of this technology’s burgeoning use in imaging.
European researchers have trained and validated an algorithm for quantifying prostate measurements on PET/CT scans.
If practices make the right moves around technology, the business outcome will help lift the specialty’s value and expand its markets.
The SOMATOM X.cite premium single-source computed tomography scanner guides radiologists through each step of the exam process.
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.