AI systems in breast imaging yield near-perfect negative predictive value but increase recall rates compared to radiologists, particularly for intermediate-risk classifications.
Key Details
- 1Study published in AJR compared Transpara v1.7.1 AI (ScreenPoint Medical) to 11 breast radiologists.
- 2Digital mammography cohort: 26,693 exams; DBT cohort: 4,824 exams.
- 3AI classified most cases as low risk but doubled or more the recall rate compared to radiologists (up to 41.8% for digital mammography at intermediate/elevated threshold).
- 4AI and radiologists both achieved NPVs of 99.8–99.9%.
- 5Sensitivity for AI at intermediate/elevated risk threshold: 94% (digital mammography), specificity fell to 58.6%.
- 6Researchers emphasized need for strategies to reduce false-positives, especially in intermediate-risk cases.
Why It Matters
While AI systems can improve workflow efficiency in breast cancer screening by safely excluding negative cases, their tendency to elevate recall rates could lead to unnecessary follow-ups and anxiety. Addressing false-positive rates is critical for successful clinical integration and radiologist workload management.
Source
AuntMinnie
Related News
•AuntMinnie
AI-Enhanced Chest X-rays Improve COPD Mortality Prediction
An open-source AI model using chest x-rays accurately predicts respiratory mortality in COPD patients, outperforming standard clinical risk grading.
•Radiology Business
Rayus Radiology Launches $40 AI Mammography Screenings in Washington
Rayus Radiology is introducing a $40 AI-enhanced mammography add-on service at clinics in Washington state.
•AuntMinnie
AI-CAD Demonstrates High Sensitivity in Breast Cancer Screening
A large study found that AI-based computer-aided detection (AI-CAD) can improve breast cancer detection and support radiologists in screening mammography.