Radiologists tend to be afraid of artificial intelligence (AI), but they should demystify it and take it for what it is, i.e. disruptive technology, according to Frank Lexa, a professor of radiology at the University of Arizona and an expert in medical leadership. “AI tools improve at the speed of light, and it’s unclear whether radiologists will keep on playing the same role. It’s only natural that they feel threatened by AI,” Lexa said. Disruptive technology consists in adopting an innovation that is lower cost and lower quality, but changes and expands the market. “Disruption changes who the market is and how you work,” he said.
In radiology, one major disruption was the development of imaging systems for non-radiologists. Some of the recently created ultrasound machines have impaired image quality, but they made the technology more available to all medical specialists. “Everyone who goes through medical training now thinks that they can do ultrasound. That’s a true disruption,” Lexa said.
Notwithstanding, disruptive technology does improve with time. This is true for ultrasound, CT and MR – for example small mobile CT scanners, and smaller MR scanners. “Soon an MRI system will fit in the back of a car,” he predicted.
More and more machines that enable non-radiologists to do imaging will emerge in the years to come, along with more machines that don’t even require a technologist. Machines that provide a preliminary read are already available. Computer systems that can extract information from imaging scans that human eyes and brains can’t see are already available.
Changing role
So far the radiologists have managed to remain in charge of the consultation, imaging supervision and interpretation. But, with the advent of AI, it is unclear what will happen. “AI has spread into radiology workflows and it’s changing the way we work. AI already helps us with scheduling and image display protocols. Stopping technologic change is not an option,” he said. AI systems that can aggregate genomic imaging and clinical information are already available. So are solutions that use machine learning to do peer review for radiologists. More will come.
To deal with these new developments, radiologists should face the reality. If they don’t, non-radiologists will. “The choice for us is change or be changed, disrupt or be disrupted, lead or be led,” Lexa said. It’s not just about the attitude, but also learning to build value and be pragmatic, by making the most of AI technology. For example, radiologists could chose to leave the boring tasks – sorting out of cases and boring cases – to the machine, so that they can deploy their full expertise exclusively for interesting cases. “A computer that makes sure I don’t make the mistake because it has high sensitivity, I would like that. Particularly if it’s late at night and if I’m working too many cases that day.” This could free radiologists up to work on collaborative research projects, new types of imaging and implementation as well as teaching, medical consultations and discussions with patients and other interesting tasks that AI can’t do – at least for now.
The future will depend on how radiologists seize the opportunities provided by AI.
Frank J. Lexa, MD, MBA, FACR is the author of the book: “Leadership Lessons for Success in Health Care”, which takes a systematic approach to developing medical leadership skills. He is an academic neuroradiologist and currently is a Professor, the Associate Chief and Vice Chair of Strategy and Leadership, in the Department of Medical Imaging at the University of Arizona. He is also the head of the Radiology Leadership Institute of the American College of Radiology.
