The point of this study is that it suggests that fully AI-automated mammography can currently deliver 70% sensitivity in detecting breast cancer using this model. It does not enable us to compare AI to unaided human performance. As this study did not include healthy controls, there is no false positive rate. The false positive rate is a crucial missing metric, since the vast majority of women do not have breast cancer.
In nearly half the false negatives from both the mammogram and DWI datasets, the cancer was categorized as occult by two breast radiologists, meaning the cancer was invisible to a trained eye. The AI model's non-occult false negative rate on the mammography data is 19.3%.
For that 19.3% figure, see Table 2: 68 non-occult in AI-missed cancer, 285 non-occult in AI-detected cancer.
This study did not compare the AI to a radiologist on a mixed set of healthy and cancer images.
In nearly half the false negatives from both the mammogram and DWI datasets, the cancer was categorized as occult by two breast radiologists, meaning the cancer was invisible to a trained eye. The AI model's non-occult false negative rate on the mammography data is 19.3%.
For that 19.3% figure, see Table 2: 68 non-occult in AI-missed cancer, 285 non-occult in AI-detected cancer.
This study did not compare the AI to a radiologist on a mixed set of healthy and cancer images.