Machine learning is emerging as one of the key hopes to change the practice of Radiology—the opportunity seems ripe, with rising calls for radiologists to demonstrate increased quality and more value, even as technology yields bigger datasets and more complexity.
But exactly how machine learning will impact the Radiology profession—and healthcare in general—remains to be seen. It will just take time and experimentation with machine learning, some say.
Keith Dreyer, DO, likens the machine learning revolution to the promulgation of electricity, which originally was used simply for lighting, but eventually ushered in a host of helpful inventions—washing machines, dishwashers, air conditioners, televisions, computers—that were previously unimaginable.
“Once you start to make machines think, taking data and performing predictive analytics, things will happen that are beyond human capability and current imagination,” said Dreyer, vice chairman of radiology at Massachusetts General Hospital and associate professor of Radiology at Harvard Medical School. “So if you could predict a group of patients that are likely to have a positive CT of the brain before it was performed, think of the advantage that would be.”
How it can help
As computers outperform humans at complex cognitive tasks, machine learning has enormous potential to enhance diagnostic accuracy, predict prognosis and, ultimately, improve patient outcomes.
J. Raymond Geis, MD, Department of Radiology at University of Colorado described how researchers at Mayo Clinic use a machine learning algorithm on a very well-defined problem of a brain tumor called glioblastomas. Different types of glioblastomas have different genetic abnormalities, and based on those genetic abnormalities, physicians treat them differently. However, radiologists looking at images of glioblastomas can’t predict which genetic variation they are. But Mayo’s machine learning program can look at this very specific clinical problem and identify genetic abnormalities.
“The advantage for radiology is in these small, well-defined clinical situations where the machines can get more information from the images than two human eyes can distinguish,” Geis says.
While still in its early stages, a critical task of machine learning in radiology is to extract more knowledge from data. “In medical imaging, we’ve dramatically increased the capability of visualization of the data, but what we haven’t improved on in the last decades is to create quantifiable data coming out of those modalities,” Dreyer says. “Once we have algorithms that are capable of doing that in an automated sense with high reliability, the output from diagnostics is going to be more consistent—the result will be much stronger predictive capabilities for diagnostics in precision care.”
But various challenges first must be overcome to achieve widespread use of machine learning. For starters, while there are thousands of different machine learning programs from hundreds of vendors on the market, each one is designed for a very specific clinical situation. But in daily practice, physicians are seeing hundreds of thousands of kinds of pathology, and it’s simply not feasible to invest in thousands of narrowly focused machine learning software programs.
That diversity of offerings is challenging for hospitals to manage. Today, many health systems are consolidating IT purchases, a trend that runs counter to that posed by machine learning offerings.
Some experts caution against being an early adopter of machine learning, contending that the technology has yet to be validated. “There is way too much emphasis on a particular capability,” said Paul Chang, MD, professor and vice chairman of radiology informatics at the University of Chicago School of Medicine. “I don’t like approaches or disruptions that concentrate on technical capability.
