"We need to evaluate the performance of the systems with datasets that are not cancer-enriched, like they were in this study," he said. "In other words, in true screening datasets. This is possible and we are planning on doing this, but it is a large effort due to the low number of cancer cases present in a real set of screening cases. So spanning such a large variety of sources and types of images and radiologist readings like we did for this study is very challenging. We also want to determine what is the best way to incorporate AI into the screening process. There are many variables to consider and evaluate in the aim of optimizing both the screening performance and its cost-effectiveness."

He adds that greater trust in AI is also essential "For this approach to be incorporated in one way or another, we need everybody involved in the screening process, including, and most importantly, the women undergoing screening, to accept the use of AI in the evaluation of the mammograms, especially if it will be used as the sole reader for some percentage of cases. This may sound scary to some people now, but we're showing that it should not be a scary thought. Medico-legal aspects need to be settled, such as when the AI system misses one cancer, who is responsible. These are the same issues being faced in all other aspects of life were AI is being introduced, like driver-less cars."


The findings were published in Journal of the National Cancer Institute.

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