San Francisco– 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.
Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.
With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)