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.

Researchers in Australia are about to begin testing, in humans, a brain-computer interface created to restore communication in people with severe paralysis.  

Auditory specialists placing Cochlear implants in hearing-impaired patients may position the devices better with an assist from AI than with conventional 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.

Machine learning analysis of Raman hyperspectroscopy—a technology used to measure the intensity of scattered laser light—has shown strong potential as a screening tool for Alzheimer’s disease when applied to an easily obtainable lab specimen: saliva.

There’s plenty of research into the diagnostic accuracy of medical smartphone apps created to supply clinical decision support (CDS). However, few studies have looked at how helpful these apps are in clinical practice.

The chances of surviving oral cancer can now be predicted from AI algorithms by measuring immune cells in tumors.

Emerging technologies like AI and robotics have vast potential to improve healthcare. Few question this. What remains unclear is how meaningful the advances will be to healthcare providers and, more to the point, the patients they serve.