It’s no secret that radiologists and medical professionals alike have been jumping on the bandwagon to better understand artificial intelligence (AI) technologies and implement them into their everyday workflow in an attempt to improve efficiency, accuracy and patient care.
However, Allison Grayev, MD, a neuroradiologist at the University of Wisconsin School of Medicine and Public Health in Madison, believes the radiologists who choose not to use AI may create a negative view of the advanced technology overall and ultimately discourage medical students from entering the field.
“Students rely on us to understand how radiology is incorporating new technology and what the future of the field will look like for them, but many of us are ill prepared to teach the younger generation about this, mostly because we ourselves are not sure,” Grayev wrote in a recent editorial published online in Academic Radiology.
According to Grayev, radiologists who are concerned or resistant about using AI should first look at the earliest ways of incorporating AI into radiology—such as PACS and electronic medical records (EHRs)—and call to mind how they've revolutionized workflow up until now.
She admits that a radiologist's resistance to implementing AI may have stemmed from having challenges in the past with implementing PACS, EHRs or technologies with computer-aided diagnosis (CAD). However, the challenges outweigh the benefits, according to Grayev, as AI has the potential to access extensive knowledge in real-time and understand questions posed in “natural language," according to Grayev.
In addition, medical students' weariness with AI is understandable, Grayev explained, as many get their information about the technology from the press and noted “splashy click bait trumpeting the demise of radiology is far more believable than the attending radiologist who can barely figure out how to send a graphics interchange format.”
Furthermore, recent studies have found that only half of attending radiologists were familiar with big data analytics, 11 percent were unfamiliar with the terms AI and machine learning (ML) and half of radiology trainees have considered a different specialty.
With resources such as the American College of Radiology’s Data Science Institute and a white paper on AI in radiology released by the Canadian Association of Radiologists, Grayev asserted that it’s the responsibility of radiologists to educate themselves on AI accordingly so they can use such techniques to their advantage in their own workflows and educate medical students about the potential of AI to advance radiology.
“If we are not willing to keep up to date with how AI is going to integrate into our specialty, we will be at consistent odds with the media and run the risk of decreasing our specialty’s appeal to medical students,” Grayev concluded. “After all, in an era where radiologists are consistently being asked to do more for less, why not outsource some of the work to our computer overlords?”