Can a computer actually diagnose you better than your GP? Machine learning is changing the face of medicine.

You’re whizzing along a dark, outback highway when the symptoms start.

Your mouth is so dry it’s getting hard to swallow or talk, your grip on the steering wheel is weakening and that centre white line just turned into two. You pull in at a small town hospital where the tired junior doctor tells you it’s probably a virus, and then hands your case over to night staff.

But the night doc comes with a difference.

She retakes your history wearing Google Glass, and the web-enabled headgear uses voice recognition to input your symptoms into a massive data base. Within seconds the cloud sends back a diagnosis.

It’s botulism; very rare, often fatal, and you’re just in time to get the antitoxin.

If it all seems a bit far-fetched the technology is, in fact, on our doorsteps. Google Glass is alive and kicking; San Francisco health start-up Augmedix is refining the internet-browsing eyeglasses to give doctors real time access to patients’ electronic health records and the web.

And Google Glass is compatible with apps, such as Isabel, that can compute the likely top diagnoses from a patient’s symptoms and, according to a 2016 review, even improve on the accuracy of clinicians.

In a world where the volume of healthcare data, including patient notes, lab tests, medications, imaging, and research articles, will soon be counted in yottabytes – that’s 10 to the power of 24 Gigabytes and enough, according to IBM, to fill a stack of DVDs that would stretch from Earth to Mars – it’s understandable doctors could use a little help.

But the march of technology is causing frissons of nervousness in medical circles, not just about how to incorporate it into everyday practice but, ultimately, whether jobs now done by doctors could one day be taken by machines.

“There is the universe of what we know, and then there is what I know,” says Herbert Chase, a physician and professor of medicine at Columbia University. “Medical practitioners can’t be expected to master the opus required to recognise all diseases,” Chase says.

“In terms of knowledge, diagnosis, optimal treatment, guideline-based care, I’m pretty sure that machines are already, in some ways, much better than we are.”

Read the full feature in the Sydney Morning Herald here

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