Radiology and AI: An algorithm took my job

Radiology18

Radiologists and those who love them sometimes seem to be some of the most insecure people around and radiology seems consistently voted the specialty most likely to be made defunct by modern technology.

In Canada, this perception has been voiced repeatedly over the past decade or two, first with the perceived threat of radiologists on the other side of the world working during our night to interpret scans and provide results when Canadian radiologists are sleeping, and now with the widespread attention given to artificial intelligence (AI) and deep learning and its potential uses in health care.

At last year’s annual meeting of the Canadian Association of Radiologists (@CARadiologists) I pondered whether this year’s meeting of that organization would be leaving it too late to discuss the significant issues revolving around AI and radiology (see my post “(A) I, Radiologist”) given the speed with which the field is advancing.

I need not have worried.

Last week the CAR released a white paper on the role of AI in radiology in advance of its annual meeting this year to be held April 26-29 on the theme of AI in radiology with the learning objectives including: discussing the recent changes that have occurred in imaging as a result of the implementation of artificial intelligence, deep learning, and machine learning in imaging workflows and; discussing the opportunities of big data and artificial intelligence to improve on the diagnostic performance and predictive value of imaging.

As an extensive overview of the topic, the CAR document squarely confronts the main issue at hand, namely that “recent breakthroughs in image recognition introduced by deep learning techniques have been equated in the media with the imminent demise of radiologists.” The authors go on to state rightly that the work of radiologists goes far beyond that of just correctly interpreting images.

“… the complex work performed by radiologists includes many other tasks that require common sense and general intelligence for problem solving–tasks that cannot be achieved through AI. Understanding a case may require integration of medical concepts from different scientific fields (eg, anatomy, physiology, medical physics) and clinical specialties (eg, surgery, pathology, oncology) to provide plausible explanations for imaging findings. Such tasks accomplished by radiologists on a daily basis include consultation, protocoling, review of prior examinations, quality control, identification and dismissal of imaging artifacts, cancer staging, disease monitoring, interventional procedures for diagnostic or therapeutic purpose, reporting, management guidance, expertise in multidisciplinary discussions, and patient reassurance”

However, the white paper’s authors also fully appreciate the potential of AI and state that “to remain current Canadian radiologists will need to follow and contribute to health care AI research and development, embrace the changes in workflow that will be required to support the implementation of clinical AI and adapt to changes in their practice that will improve care of their patients.”

So, while the demise of radiology (again) does not seem imminent, the white paper will bear close reading by Canadian radiologists who wish to remain relevant with some of the most significant and fundamental advances in medicine currently underway.

(Image courtesy of the Canadian Association of Radiologists)