AI software as a third reader in breast cancer screening—a prospective diagnostic observational study

Abstract

Question Does the integration of AI software as an independent third reader improve cancer detection rates in mammography screening without increasing false-positive findings and recall rates? Findings AI as an independent third reader increased cancer detection by 9.5%, mainly identifying Luminal-A-like cancers, significantly decreasing the positive predictive values of cases referred to at the consensus conference and increasing the number of recalled cases. Clinical relevance Using AI as an independent third reader enhances mammographic cancer detection by offering radiologists complementary sensitivity, especially for low-risk lesions. However, maintaining human readers is essential, as AI may miss aggressive subtypes like triple-negative breast cancers.

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