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How Averaged Data Hid Opposite Effects in a Key Radiology AI Study

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A 2013 mammography study involving 50 radiologists found that computer-aided detection (CAD) had no measurable average effect on diagnostic accuracy, leading the medical field to largely move on. In 2013, researchers at City University London reanalysed the same data by separating radiologists into subgroups rather than pooling them together. They discovered that CAD actually improved sensitivity for the 44 least skilled radiologists on easier cases, while significantly reducing sensitivity for the 6 most skilled radiologists on the hardest, highest-stakes cases. Because these two opposing effects cancelled each other out, the original aggregate result reported nothing had changed. The case illustrates a broader problem with averaged metrics: a single aggregate figure can mask sharply divergent outcomes across different user groups, making it unreliable for guiding deployment or operational decisions.

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