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Has ‘moderate’ artificial intelligence in diagnostic imaging arrived?

June 16, 2016
From the June 2016 issue of HealthCare Business News magazine

Several startup and larger companies are now claiming an analogous achievement to the development of a “deep Q-network” for their ability to use large numbers of imaging studies to “learn” how to interpret them for diagnosis. I am very skeptical of these current claims and am not aware of any companies that come close to this capability. In fact, no one has responded to my challenge that I’ll go anywhere to wash the car of anyone who can defeat a fifth grader at simply finding the adrenal gland on CT. The task of image interpretation is far more complex than recognizing a score on a TV screen with a joystick controller and red button. What I have seen these companies do is “re-invent/ re-discover” approaches that have been utilized in “weak AI,” using statistical and “machine learning” algorithms that have already been well described and developed in the scientific literature for decades.

AI doesn’t need to be limited to making findings on a diagnostic imaging study. There are many areas in which our interoperability, graphical user interfaces and decision support systems can benefit tremendously from weak AI, to say nothing of “moderate” AI. This will be our area of greatest opportunity and potential in the next several years, and I believe it will change the practice of radiology many years before we start talking about revolutionary, rather than evolutionary, advances in diagnostic image interpretation. I have been asked to present this year on the following topics at two industry events:

• SIIM Closing Session: “Peering into the Future through the Looking Glass of Artificial Intelligence,” Portland, Oregon, July 2016.
• RSNA 2016: Controversy Session Topic: “Elementary, My Dear Watson: Will Machines Replace Radiologists?” and “The Promise of Machine Learning (and pattern recognition) in Radiology.”

About the author: Eliot Siegel is a professor at the University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine. He also works for the VA Maryland Healthcare System in Baltimore.

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