Evidence
Closing the Performance Gap between Generalists and Breast Imaging Specialists Using a Nationally Deployed AI Workflow for Screening Mammography
General radiologists interpret most screening mammograms in the United States and previous research has reported a gap in screening performance between general radiologists and fellowship-trained breast imaging specialists. This study examined clinical performance before and after implementation of a multistage artificial intelligence-supported workflow, with the goal of evaluating product impact on general radiologists compared to breast imaging specialists.
Equitable Impact of an AI-driven Breast Cancer Screening Workflow in Real-World US-Wide Deployment
Nature Health
Louis et al. Nov 2025
Artificial intelligence shows promising results for improving early breast cancer detection and overall screening outcomes. Here the AI-Supported Safeguard Review Evaluation (ASSURE) study evaluates an AI workflow on digital breast tomosynthesis exams from women across four states to optimize early cancer detection. Implementation of the AI workflow improved screening effectiveness with equitable benefits.
Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists
Radiology Artificial Intelligence
Kim et al. Feb 2024
The performance of 18 general radiologists and breast imaging specialists (9 generalists, 9 specialists) was evaluated with and without the aid of a custom-built categorical AI system. The categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics.
Robust Breast Cancer Detection in Mammography and Digital Breast Tomosynthesis Using an Annotation-Efficient Deep Learning Approach
Nature Medicine
Lotter et al. Jan 2021
DeepHealth’s deep-learning algorithm achieves state of the art performance in mammogram classification. The AI model was compared to 5 readers in a reader study of 131 index cancers and 154 confirmed negatives. The model showed robust and generalizable performance, reporting an Area Under the Curve (AUC) of 0.945 and outperforming all radiologists with a sensitivity 14% higher than the average radiologist sensitivity.