A meta-analysis of 14 studies has shown AI algorithms correctly diagnose diseases in medical imaging around 87% of the time while ruling out specific diseases with 93% accuracy.

“Fast MRI” scans, which use quicker imaging techniques and don’t require sedation or ionizing radiation, can identify traumatic brain injuries (TBIs) in young patients, according to a new study published in Pediatrics.

MRI can help diagnose minor stroke and transient ischemic attacks (TIA) in patients who experience symptoms that aren’t always associated with stroke, according to a multi-year study published Sept. 23 in JAMA Neurology.

Deep-learning analysis of eye scans has proven superior to conventional analysis of the same images for the task of detecting and tracking vision diseases like glaucoma and age-related macular degeneration.

Artificial intelligence (AI) is still gaining momentum in 2019 as researchers build bigger and better algorithms and vendors release new, state-of-the-art solutions. For healthcare providers, however, the world of AI can still be intimidating in a lot of ways; if you haven’t already started exploring the potential of AI, where do you begin? What’s the first step?

New research out of the University of Colorado School of Medicine has shown that “fast MRI” can effectively identify traumatic brain injuries in kids—a good alternative to CT scanning.

Screening mammography was shown to identify more cancers than the average detection rate in men at a high risk for breast cancer, according to a new study published in Radiology. The tool may save lives by detecting cancers earlier than other screening methods.

Portable ultrasound maker Fujifilm SonoSite has turned to the nonprofit Allen Institute for Artificial Intelligence’s AI2 Incubator for help harnessing AI’s image-interpretation potential.