Microsoft has updated its computer vision app for the visually challenged people with an option to explore photos by touching them.
The new feature in the “Seeing AI” iOS app enables users to tap their finger to an image on a touch-screen to hear a description of objects within an image and the spatial relationship between them.
“Users can explore photos of their surroundings taken on the ‘Scene’ channel, family photos stored in their photo browser, and even images shared on social media by summoning the options menu while in other apps,” Saqib Shaikh, Software Engineering Manager and Project Lead for ‘Seeing AI’, said in a blog-post late Tuesday.
Seeing AI is already helping users read printed text in books, restaurant menus, street signs and handwritten notes, as well as identify banknotes and products via their barcode.
Leveraging on-device facial-recognition technology, the app can even describe the physical appearance of people and predict their mood.
“For the first time we’re releasing iPad support, to provide a better ‘Seeing AI’ experience that accounts for the larger display requirements,” Shaikh added.
As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.
In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.
The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.
“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.
Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.
According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.
The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.
To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.