Established Artificial intelligence (AI) models have shown potential for improving breast cancer screening outcomes using mammography, however, their performance across various subgroups of patient demographics, imaging characteristics and pathology subtypes remains unclear. In here, the authors find that the application commercial AI model (Lunit INSIGHT DBT) for breast cancer detection on digital breast tomosynthesis (DBT) demonstrates robust performance across demographics, however, this is reduced for Ductal Carcinoma in Situ (DCIS), calcifications, and dense breasts.
- Beatrice Brown-Mulry
- Rohan Satya Isaac
- Hari Trivedi
