Below is a summary for the article : Google to commercialize artificial intelligence to detect diseases by The Investor
With the recent advances in artificial intelligence technology, Google has been working to apply a form of high-level AI computing known as deep learning to the field of medicine and health care.
Though further developments are underway, Google said on April 27 that it has successfully developed new deep learning algorithms that can detect and diagnose diabetic retinopathy, an eye disease which can lead to blindness, as well as locate breast cancer.
Lily Peng, product manager of the medical imaging team at Google Research, shared how the US tech giant is using deep learning to train machines to analyze medical images and automatically detect pathological cues, be it swollen blood vessels in the eye or cancerous tumors, during a video conference with the South Korean media hosted by Google Korea.
On the medical front, Google has made significant progress on building an algorithm to read retinal scan images to discern signs of diabetic retinopathy, the fastest growing cause of preventable blindness in the world.
Google designed an AI algorithm to analyze retinal images and identify features of diabetic retinopathy.
As the algorithm has shown high accuracy, Google has now moved to build an interface and hardware into which doctors in India can input a retinal image and immediately receive a grade for diabetic retinopathy.
Another field spearheading Google’s deep learning push is cancer detection.
Google’s AI algorithm achieved a tumor localization score – how accurately it can locate the cancerous tumor – of 0.89, exceeding the score of 0.73 from a highly-trained human pathologist with unlimited time for examination.
Google’s algorithm located tumors with a sensitivity of 92 percent, but this was when set to allow eight false positive readings per slide.
The Google researcher said it will take some time before devices running on Google’s deep learning algorithms are commercialized for use in the medical sector, as it must secure sufficient clinical data proving their efficacy and accuracy before seeking regulatory approval.
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