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Another benefit AI brings with this software is the built-in decision support. This allows radiologists to make more precise evaluations of lesions and calcifications. The tool works interactively. If a radiologist sees an anomaly on a mammogram or tomosynthesis, he or she can click on the suspicious region. The software then shows, based on a score of 1 to 95, how high the chance is that a malignancy is present. Mann: “If a finding scores very low, I know that it is likely to be a benign anomaly and I can ignore it.”

For diagnostic imaging, the breast clinic at Radboud University Medical Center uses tomosynthesis to acquire 3D images of the breast. This technique provides a higher depth of resolution, and therefore tissue separation, than conventional 2D mammograms, but it also generates a greater number of images for evaluation. Mann: “As opposed to one mammogram, you have sixty tomosynthesis slices. In practice, that means that we need twice as much reading time to evaluate a ‘tomo’.”

This “extra time” is also partly made up for by using the decision support tool in the AI software. It screens the images like a virtual radiologist, which enables human radiologists to make their evaluations faster – 15 to 20 percent faster, as various studies have shown.[1] As a result, radiologists have more time for more complex cases.

Nevertheless, Mann realizes that many steps must be taken before that. “From an ethical standpoint alone, it is still hard to say to a woman: the computer has evaluated the images and confirmed that there is nothing to worry about. The patient expects a ‘real’ doctor to look at the image. At least for now. But AI learns extremely fast. The more images we can use to let the system fine-tune itself, the higher the quality of the system and the care will be. Incidentally, it does raise new questions from the standpoint of privacy. If we can find answers to them, the quality and efficiency of breast diagnostics will make even greater leaps forward.”