Has Technology Ruined Your Chance Of Employment In Radiology?
Has Technology Ruined Your Chances of Employment in Radiology?
Among the many benefits of living in the Computer Age are the rapid technological advancements that continue to bring convenience and joy to our daily lives. From handheld devices with 24/7 internet access to cars that drive themselves, the future many hoped for (and dreamed of) is happening right now. But while the positive aspects of new technologies mostly outweigh the negatives, disruptive change naturally creates both winners and losers, particularly on the employment front. The medical field is not immune to this phenomenon.
In the recent past, victims of technological encroachment tended to be lower skilled workers whose roles could be easily automated. Today however, potential job automation targets include professionals in high-skill fields ranging from law to engineering to medicine. In short, automation is now “blind to the color of your collar”, according to Jerry Kaplan, author of “Humans Need Not Apply”, (https://www.amazon.com/Humans-Need-Not-Apply-Intelligence/dp/0300213557) a sobering book that sheds light on the uncertain future facing modern workforces.
All of this is a roundabout way of asking a very uncomfortable question: Are robots coming for your radiology job?
The short answer is no…but don’t let your guard down. Here’s why.
Today the poster child of artificial intelligence (AI), IBM’s “Watson”, can already find clots in pulmonary arteries. And unlike a busy radiologist who might read 20,000 or so studies per year, Watson is on target to review 30 billion medical images (http://www.medscape.com/viewarticle/863127) It goes without saying that Watson’s only going to get better.
What’s more, a number of Silicon Valley startups are currently applying new technologies to automate and improve the delivery of medicine. One firm in particular, Enlitic, is even developing a deep-learning system that uses AI to analyze X-ray and CT scans. According to an article in the Economist, (http://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety) Enlitic’s system has performed 50% better in tests than a group of three expert radiologists at classifying malignant tumors. When used to examine X-rays, their deep-learning system also significantly outperformed human experts. Of course, this emerging technology leaves much to be desired in the bedside manner department, but that’s what robot doctors (http://www.techtimes.com/articles/131870/20160209/will-robots-in-healthcare-make-doctors-obsolete.htm) are for.
Now before you go and trade your radiology degree for a barista outfit, consider the fact that according to most experts, including the CEO of Elitic himself (Igor Barani, MD, a radiation oncologist), artificial intelligence and radiologists aren’t diametrically opposed. In fact, they’re largely symbiotic. By design, AI will increasingly free radiologists from mundane tasks that can be automated, like reviewing CT scans for lung nodules. As Barani puts it, “tasks that can be automated should be given to the machine—not as surrender but secession.” This outlook portends a future in which radiologists are increasingly empowered to deliver better patient care, not supplanted by robotic overlords.
Regardless of what technology naysayers say, there will always be radiology careers for talented individuals (http://scpmgphysiciancareers.com/) to pursue. That being said, the role of radiologists will almost certainly narrow in the coming years and decades to one of inference, not detection — and that’s an important takeaway. With little doubt, the medical field will require fewer radiologists per capita because of deep learning technologies that simply do a better job of identifying anomalies. The successful radiologists of tomorrow will be the ones who can reduce AI-generated data into useful information that helps patients get better, faster. That’s not a future to be scared of; it’s one all current and prospective radiologists should eagerly anticipate.