Hybrid AI-Human Mammography Reading Cuts Workload Without Compromising Cancer Detection
A hybrid AI and radiologist reading strategy for screening mammography reduced radiologist workload by 38% without affecting recall or cancer detection rates.
Key Details
- 1A Radboud University Medical Center team trialed a hybrid AI-human method for reading screening mammograms.
- 2Radiologist workload dropped by 38% while recall (23.6%) and cancer detection rates (6.6%) matched standard double reading.
- 3Study included 41,469 mammograms from 15,522 women in the Dutch National Breast Cancer Screening Program, spanning from 2003–2018.
- 4The AI (ScreenPoint Medical) handled confident predictions; radiologists reviewed cases with uncertain AI output.
- 5Uncertainty estimation (entropy of probability of malignancy) was key to triaging cases.
- 6Only one cancer case was missed by AI but caught by radiologist using this hybrid approach (ductal carcinoma in situ).
Why It Matters

Source
AuntMinnie
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