AI hybrid strategy improves mammogram interpretation

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection rates.

“Although the overall performance of state-of-the-art AI models is very high, AI sometimes makes mistakes,” said Sarah D. Verboom, M.Sc., a doctoral candidate in the Department of Medical Imaging at Radboud University Medical Center in the Netherlands.

“Identifying exams in which AI interpretation is unreliable is crucial to allow for and optimize use of AI models in breast cancer screening programs.”

The hybrid reading strategy involves using a combination of radiologist readers and a stand-alone AI interpretation of cases in which the AI model performs as well as, or better than, the radiologist.

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