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There’s a scene in Big Hero 6 — the Disney movie featuring a balloon-like healthcare robot named Baymax — where the cuddly humanoid device tries to diagnose a grumpy teenager in his care.
The lanky 14-year-old, Hiro, is wedged against the wall after falling off his bed. Then, one by one, objects from his shelf fall on top of him with a thud.
“On a scale of 1 to 10, how would you rate your pain?” Baymax asks, repeatedly.
A suitably robotic delivery, sure, but throughout the film, the marshmallow-like medical assistant shows a knack for helping out — perhaps better than most humans — and offering a dose of empathy while he’s at it.
So… is that the future of medicine? Robot-based artificial intelligence replacing doctors and nurses? Experts say not quite — or at least not yet. But we are now seeing how AI can impact everything from medical research to the diagnosis process to patient care.
AI Will First Impact Research and Diagnostics
“I think it’ll be a revolutionary technology,” says Jon Aikman, an Adjunct Professor at Queen’s University and the University of Toronto, an Investment Manager, and AI Expert who teaches graduate courses in AI, Fintech, and Finance.
Behind-the-scenes in healthcare, there’s already a “huge, huge drop” in the time it takes to develop research thanks to machine learning, he says. “You’re getting more insight than ever before.”
Current AI technology is also proving to be a powerful diagnostic tool. One study from China, for instance, found a deep-learning system was better than some doctors at diagnosing common childhood ailments like asthma, pneumonia, and mouth-related diseases reported Quartz in February.
Elsewhere researchers in England were able to develop an AI diagnostics system that’s more accurate than physicians at diagnosing heart disease, at least 80 percent of the time; and one research team’s “smart” microscope technology was able to detect potentially-deadly blood infection bacteria 95 percent of the time after training on a series of 100,000 images, according to Futurism.
And when it comes to catching cancer, the online tech and science magazine reports a Japanese study found a computer-assisted endoscopic system was able to spot potentially-cancerous growths in the colon with 86 percent accuracy.
The Sky’s the Limit for AI in Healthcare
“Many in the medical community were frankly surprised by what deep learning could accomplish,” writes physician Eric Topol in his new book Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.
“Although there must be some limit at which the learning stops, we haven’t reached it yet,” he adds. “And, unlike humans who get tired, have bad days, may get emotional, sleep deprived, or distracted, machines are steady, can work 24/7 without vacations, and don’t complain.”
The shift might be comforting news to patients. But for medical professionals, there might be some worries about technology taking over in a clinical setting.
That concern could be a bit overblown, according to Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and co-author of Prediction Machines: The Simple Economics of Artificial Intelligence.
For something to be proven to be useful clinically, it takes time, research, and requires going through a regulatory process, he explains.
But if a key aspect of your role is making predictions, “a machine is increasingly going to be doing that aspect of your job,” Goldfarb adds.
Aikman agrees. Hematologists — specialists who handle blood-related conditions — and radiologists — who use medical imaging like X-rays to diagnose and treat illnesses and injuries — could both see AI handling bigger chunks of their jobs in the years ahead, he explains.
AI Will Create New Opportunities (and Improve Treatment)
As AI and automation take on more work in the healthcare space, it doesn’t mean that humans are going to be pushed out. In the case of radiology, Goldfarb says a big chunk of the workflow is transcribing what the clinicians are saying into a microphone. Increasingly, that process can be done by machines, freeing up the human workers to focus more on the care.
Goldfarb believes other exciting opportunities are on the horizon as well, including more preventative and personalized medicine.
AI could increasingly help to anticipate patients’ health problems, he says, giving clinicians the opportunity to use preventative strategies to make sure major health issues don’t arise — or, at the very least, are pushed off far into the future.
Machine learning and increased data collection on individual patients could also help zero-in on targeted treatments, developed not just for someone’s genetic background but also the unique qualities of their health and emotional state.
“So the right patient gets the right treatment at the right time,” Goldfarb says.
As for whether or not the healthcare field could ever look like the movies, where robots instead of Doctors are poking and prodding patients, he says that humanoid form of AI — or artificial general intelligence — is quite different from the technology currently being developed.
But that doesn’t mean it’s impossible.
“The AI research world has been talking about the hope for artificial general intelligence,” Goldfarb says. “Experts today say it’s still 20 to 50 years away — it’s not forever.”