HOW CAN AI PREVENT BLINDNESS FOR MILLIONS OF PEOPLE
When you take a picture of a dog or a cat, Google's algorithms place it in a folder marked as "pets", with no direction from you. Here you are seeing the benefits of image recognition AI.
The same technology is used by doctors to diagnose diseases on a scale never before possible by humans.
Diabetic retinopathy is caused by type two diabetes and is one of the fastest-growing causes of preventable blindness. Each of the more than 415 million people living with the disease risks losing their eyesight unless they have regular access to doctors.
In India, there are simply too many patients for the number of doctors to treat. There are 4,000 diabetic patients for every ophthalmologist in India, where the US has one for every 1,500 patients.
In other developing nations, more than 80% of sufferers live in places with little or no access to care. These people are going blind, because of poverty.
That is where companies such as Verily come in. It is one of Google's many sister-companies under Alphabet, that chose diabetic retinopathy as the entry-point for massive scale neural network-powered medical insights.
It works only through data that is captured. Today's algorithms and deep learning networks are well-suited for processing the individual segments and pixels in an image and classifying the image in one of any number of categories. For example, Google’s ImageNet (the core visual recognition AI for the company) has more than 22,000 categories containing at least 14 million images.
AI can diagnose diabetic retinopathy in the exact same way it determines whether something is a hotdog or not – which is also how physicians do it.
Doctors diagnose diabetic retinopathy through interpreting retina scans. It is similar to examining an X-ray or MRI, where the physician scans the images for specific indications of abnormal markers. They have to be on the lookout for unrelated artefacts such as dust or lens flares, but otherwise, it’s just a matter of looking for specific markers.
Over the past three years, machine learning developers have achieved several breakthroughs in creating image recognition AI. They have legitimately reached a point where a computer’s ability to perform medical diagnostics based on reviewing images exceeds that of humans (in very specific use cases).
Right now, anyone on this planet at risk of type two diabetes, who do not have viable access to a medical practitioner who can diagnose these diseases, is playing roulette with their health.
It’s not just vision that’s at stake: diabetes can ravage the liver and greatly increase the risk of cardiovascular problems – among a myriad of other damaging or potentially fatal effects.
However, AI might be able to completely solve this problem. According to Lily Peng, Product Manager for the Medical Imaging team at Google Research, the answer for overwhelmed physicians is offloading the parts of their jobs that can be done by machines:
"Deep learning is good for tasks you’ve done 10,000 times and on the 10,001st time, you’re sick of it. This is really good for the medical field."
AI will not be your doctor in the future, its merely there to assist your physician right now. Jessica Mega, Verily’s Chief Medical Officer, believes that machine learning can streamline the diagnostic process:
"Is technology going to replace physicians or going to replace the healthcare system? The way I think about it, it just augments the work we do. If you think about the stethoscope it was invented about 200 years ago. It doesn’t replace the physician is just augments them."
Many of the challenges facing the healthcare industry can be solved by machine learning, but the developers working on these problems can’t do it alone. Google, Verily, IBM, Intel, Microsoft, and hundreds of other companies are racing against the clock to find a way to make the field of medicine a proactive discipline that prevents disease instead of the reactive one it is.
It’s time to rethink the way we approach caring about ourselves and each other. Machine learning provides a way for doctors to stop being data analysts and become the patient care-providers the field so desperately needs.