Google to supercharge healthcare with artificial intelligence

Google has made several statements about its aim to improve the quality of life using technology that is interesting. 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. On April 27, 2017, Lily Peng, product manager of the medical imaging team at Google Research, announced that “Alphabet Inc. has begun to commercialize artificial intelligence and machine learning and making them available to users, developers, and enterprises.”

Eminent breakthroughs by Google’s AI algorithms

Diabetic retinopathy: 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.

Cancer detection: Google is also developing another deep learning algorithm that surveys biopsy images to locate metastatic breast cancer that has spread to the lymph nodes. The algorithm was tested against slides containing 10,000 to 400,000 images, of which 20 to 150,000 showed tumors.

Google’s next-generation Tensor Processing Unit

During the opening remarks of the I/O keynote held on 17th May, CEO Pichai announced Google’s next-generation Tensor Processing Unit, a specially designed chip for machine learning that works on the company’s TensorFlow platform. This new TPU chip effectively makes building AI on Google’s platform incredibly fast and efficient. With TPU and TensorFlow being optimized to work together, Google is effectively transforming its cloud computing platform into the Android for AI. This is a big opportunity because this means that Google can now have more powerful machine learning and AI capabilities to offer to different fields including healthcare.

However, 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.

Leave a Reply

* Please perform CAPTCHA test