Quote Of The Year

Quote Of The Year - Paul Shetler - "Its not Your Health Record it's a Government Record Of Your Health Information"

Wednesday, February 28, 2018

There Is Little Doubt The Game Is Changing - And Maybe Faster Than We Expect.

This appeared last week:

Google's neural networks detect heart attack risk by looking at patients' eyes

Drew Harwell & Carolyn Y. Johnson
Published: February 20 2018 - 1:50PM
By looking at the human eye, Google's algorithms were able to predict whether someone had high blood pressure or was at risk of a heart attack or stroke, researchers at the company have confirmed, opening a new opportunity for artificial intelligence in the vast and lucrative global health industry.
The algorithms didn't outperform existing medical approaches such as blood tests, according to a study of the finding published in the journal Nature Biomedical Engineering. The work needs to be validated and repeated on more people before it gains broader acceptance, several outside physicians said.
But the new approach could build on doctors' current abilities by providing a tool that people could one day use to quickly and easily screen themselves for health risks that can contribute to heart disease, the leading cause of death worldwide.
"This may be a rapid way for people to screen for risk," Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email. "Diagnosis is about to get turbo-charged by technology. And one avenue is to empower people with rapid ways to get useful information about their health."
Google researchers fed images scanned from the retinas of more than 280,000 patients across the United States and United Kingdom into its intricate pattern-recognising algorithms, known as neural networks. Those scans helped train the networks on which telltale signs tended to indicate long-term health dangers.
Medical professionals today can look for similar signs by using a device to inspect the retina, drawing the patient's blood or assessing risk factors such as their age, gender, weight and whether they smoke. But no one taught the algorithms what to look for; instead, the systems taught themselves by reviewing enough data to learn the patterns often found in the eyes of people at risk.
The true power of this kind of technological solution is that it could flag risk with a fast, cheap and noninvasive test that could be administered in a range of settings, letting people know if they should come in for follow-up.
Lots more here:
I am sure that this is just the ‘thin end of the wedge’ as they say and that we will see more and more systems trained on vast data-sets to out-diagnose the diagnosticians. The time is slowly but surely coming!

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