A real-world study found no significant improvement in breast cancer detection or recall rates with an FDA-approved AI mammography tool in a single-reader setting.
Key Details
- 1Study reviewed 24,520 screening mammograms post-AI and 21,630 pre-AI, all using digital breast tomosynthesis (DBT).
- 2Recall rate was 12.3% before and 12.8% after AI implementation (p=0.13).
- 3Cancer detection rate per 1,000 women was 6.7 pre-AI and 7 post-AI (p=0.69), indicating no significant difference.
- 4AI categorized 61.3% as low risk, 34.9% as intermediate, and 3.8% as elevated risk, with cancer detection rates in those groups at 0.5, 8.4, and 98 per 1,000, respectively (p<0.001).
- 5AI missed 15 out of 171 diagnosed cancers, mostly among low- and intermediate-risk cases not flagged by AI.
- 6Authors suggest AI may aid in patient risk stratification, but primary screening metrics did not improve.
Why It Matters
While prior European studies in double-reading environments showed AI could improve detection or reduce workload, this large U.S. study indicates no significant breast cancer screening benefit in single-reader practice. The findings highlight the need for more nuanced evaluation of AI tools in real-world clinical workflows and may guide future U.S. AI adoption and regulatory considerations.
Source
AuntMinnie
Related News
•AuntMinnie
AI Advances in Breast Cancer Risk, CEUS Training Updates, and Imaging AI Variability
This week's top radiology news reviews AI advances in breast and lung cancer risk prediction, new CEUS training standards, and prostate screening updates with imaging modalities.
•Radiology Business
DeepHealth's Prostate AI Gains FDA Clearance for MRI Workflows
DeepHealth, a RadNet subsidiary, received FDA clearance for its AI solution to assist prostate cancer detection and MRI workflow.
•AuntMinnie
Mammography AI Model Outperforms Rivals for 10-Year Breast Cancer Risk
A new mammography-based AI model predicts 10-year breast cancer risk more accurately than established clinical and AI models.