Researchers have identified an approach for more accurately diagnosing schizophrenia using AI, bringing some objectivity to the field of neurodevelopmental disorder diagnosis, according to a study published in the May edition of Artificial Intelligence in Medicine.
A combination of the established denoising algorithm NeighShrink and chi-square unbiased risk estimation (CURE) could reduce noise in magnetic resonance (MR) images more effectively than traditional methods, according to research published in Artificial Intelligence in Medicine.
The American College of Radiology Data Science Institute (ACR DSI) introduced a new software platform April 5 aimed at better engaging radiologists in the creation, validation and use of AI models.
A team at Weill Cornell Medicine has developed an AI algorithm that can identify whether human embryos fertilized in vitro have the potential to progress to successful pregnancies, offering guidance as few as five days after an embryo is implanted.
A new AI software can quickly and accurately determine the manufacturer and model of a cardiac rhythm device from an x-ray, possibly speeding up treatment when the devices fail.
Pittsburgh-based startup SpIntellx has been awarded a $225,000 research grant by the National Science Foundation to further develop its HistoMapr-Breast system—an AI that images whole-slide samples and acts as a computational guide for pathologists.
Canadian researchers working with Toronto General Hospital-University Health Network have developed a natural language processing (NLP) approach to predicting downstream radiology resource utilization, according to work published in the Journal of the American College of Radiology March 2.
Though the idea of artificial intelligence displacing radiologists worries more than half of surveyed medical students interested in an imaging career, radiology programs have seen a spike in applications in recent years, according to work published in Academic Radiology.
AI systems can detect breast cancer just as well as radiologists, according to a study published March 5 in the Journal of the National Cancer Institute.
Zebra Medical Vision (Zebra-Med), an Israel-based deep learning imaging analytics company, announced it was granted CE certification for two of its AI-based products intended to speed up clinical review and diagnosis. One is for pneumothorax in chest x-rays and the other helps radiologists detect brain bleeds in CT scans.
Aidence, a Netherlands-based medical imaging company, has raised about $11.3 million in funding to expand its AI-powered platform for detecting lung cancer.
There are four areas in which AI must excel to become clinically viable in women’s medical imaging—particularly mammography: performance, time, workflow and cost, according to an opinion article published in theAmerican Journal of Roentgenology.
