Current Featured Articles
- Stanford launches huge dataset of chest x-rays for AI
Researchers from Stanford University have released a massive dataset of more than 224,000 chest x-rays to be used for the development of artificial intelligence (AI) algorithms. The group has also launched a competition for developers of AI algorithms to test their models.
- Should radiologists become gatekeepers against overuse?
Should the primary duty of radiologists shift from interpreting images to preventing unnecessary orders for medical imaging? That's just one of several novel suggestions for reducing radiology overuse in a Viewpoint article published online January 7 in the Journal of the American Medical Association.
- Tissue enhancement on breast MRI informs cancer risk
Elevated levels of background parenchymal enhancement on breast MRI increase a woman's risk of developing invasive breast cancer -- even more than breast tissue density does, according to a study published online January 9 in the Journal of Clinical Oncology.
- CT radiation dose levels vary widely, new study finds
A new international study has found wide variation in radiation dose levels used for CT scans, potentially exposing patients to unnecessary radiation. On the positive side, dose could easily be reduced through changes in CT protocols, according to the study published January 2 in BMJ.
- AI can differentiate glioblastoma, brain metastasis
The combination of radiomics and an artificial intelligence (AI) algorithm can yield a high rate of accuracy for differentiating between glioblastoma and solitary brain metastasis on T1-weighted MRI exams, according to research published online January 11 in the Journal of Magnetic Resonance Imaging.
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