Terms like “machine learning,” “artificial intelligence” and “deep learning” have all become science buzzwords in recent years. But can these technologies be applied to saving lives?
The answer to that is a resounding yes. Future developments in health science may actually depend on integrating rapidly growing computing technologies and methods into medical practice.
Cosmos spoke with researchers from the University of Pittsburgh, in Pennsylvania, US, who have just published a paper in Radiology on the use of machine-learning techniques to analyse large data sets from brain trauma patients.
Co-lead author Shandong Wu, associate professor of radiology, is an authority on the use of machine learning in medicine. “Machine-learning techniques have been around for several decades already,” he explains. “But it was in about 2012 that the so-called ‘deep learning’ technique became mature. It attracted a lot of attention from the research field not only in medicine or healthcare, but in other domains, such as self-driving cars and robotics.”
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So, what is deep learning? “It’s a kind of multi-layered, neural network-based model that is constantly mimicking how the human brain works to process a large set of data to learn or distill information,” explains Wu.
The key to the increased “maturity” of machine-learning techniques in recent years is due to three interrelated developments, he says. These are the technical improvements in the algorithms of machine learning; the developments in the hardware being used, such as the improved graphical processing units; and the large volumes of digitised data readily available.
That data is key. Lots of it.
Machine-learning techniques use data to “train” the model to function better, and the more data the better. “If you only have a small set of data, then you don’t have a very good model,” Wu explains. “You may have very good questioning or good methodology, but you’re not able to get a better model, because the model learns from lots of data.”
Even though the available medical data is not as large as, say, social media data, there is still plenty to work with in the clinical domain.
Machine-learning models and algorithms can inform clinical decision-making, rapidly analysing massive amounts of data to identify patterns, says the paper’s other co-lead author, David Okonkwo.
“Human beings can only process so much information. Machine learning permits orders of magnitude more information available than what an individual human can process,” Okonkwo adds.
Okonkwo, a professor of neurological surgery, focuses on caring for patients with brain and spinal cord injuries, particularly those with traumatic brain injuries.
“Our goal is to save lives,” says Okonkwo. “Machine-learning technologies will complement human experience and wisdom to maximise the decision-making for patients with serious injuries.
“Even though today you don’t see many examples, this will change the way that we practise medicine. We have very high hopes for machine learning and artificial intelligence to change the way that we treat many medical conditions – from cancer, to making pregnancy safer, to solving the problems of COVID.”
But important safeguards must be put in place. Okonkwo explains that institutions such as the US Food and Drugs Administration (FDA) must ensure that these new technologies are safe and effective before being used in real life-or-death scenarios.
Wu points out that the FDA has already approved about 150 artificial intelligence or machine learning-based tools. “Tools need to be further developed or evaluated or used with physicians in the clinical settings to really examine their benefit for patient care,” he says. “The tools are not there to replace your physician, but to provide the tools and information to better inform physicians.”
Evrim Yazgin has a Bachelor of Science majoring in mathematical physics and a Master of Science in physics, both from the University of Melbourne.
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