Enterprise AI Analysis: Healthcare AI
Equitable impact of an AI-driven breast cancer screening workflow in real-world US-wide deployment
Published: 17 November 2025 by Leeann D. Louis, Edgar A. Wakelin, Matthew P. McCabe, Annie Y. Ng, Jiye G. Kim, Christoph I. Lee, Diana S. M. Buist, A. Gregory Sorensen, Bryan Haslam
This study demonstrates that a novel AI-driven workflow for breast cancer screening significantly enhances early cancer detection in a real-world US setting. The implementation of this multistage AI workflow led to a +21.6% increase in the Cancer Detection Rate (CDR), improving it from 4.6 to 5.6 per 1,000 exams. Crucially, these benefits were found to be equitable across all racial and breast density subpopulations, with CDR increases ranging from 20.4% to 22.7% and no observed disparities in outcomes. The workflow, integrating AI-based computer-aided detection and an AI-driven safeguard review by specialists for at-risk cases, also resulted in a +15.0% increase in positive predictive value (PPV1), signifying more appropriate recalls. This large-scale deployment across four US states validates AI's potential to improve screening effectiveness and equity in diverse healthcare environments.
Executive Impact
A novel AI workflow in breast cancer screening delivers substantial improvements in cancer detection rates with proven equitable benefits across diverse patient populations in real-world US deployment.
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Increase in Cancer Detection Rate (CDR)
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Increase in Positive Predictive Value (PPV1)
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Exams Processed by AI Workflow
Deep Analysis & Enterprise Applications
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+21.6%
Increase in Cancer Detection Rate (CDR)
The AI-driven workflow significantly improved early breast cancer detection across the US, detecting 21.6% more cancers compared to the standard of care (5.6 vs 4.6 per 1,000).
AI-Driven Breast Cancer Screening Workflow
Images Acquired
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Initial AI Interpretation (CADe/x)
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AI-driven Safeguard Review (for at-risk cases)
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Breast Imaging Specialist Review (if needed)
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Final Interpretation Report
The workflow integrates AI-based computer-aided detection (CADe/x) for initial interpretation and an AI-driven safeguard review by a breast imaging specialist for cases identified as at-risk but not recalled initially.
| Category | AI Workflow Benefits |
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| Racial & Ethnic Subpopulations |
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| Breast Density Subpopulations |
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A crucial finding highlights the equitable impact of the AI workflow, showing consistent improvements and no new disparities in outcomes across different racial/ethnic groups and breast densities, addressing a significant need in US healthcare.
Real-World US-Wide Deployment
Successful Implementation Across Diverse Outpatient Settings
The ASSURE study represents a large-scale, real-world deployment of the multistage AI-driven workflow across 5 practices (109 sites, 96 radiologists) in California, Delaware, Maryland, and New York. This demonstrates the scalability and practical effectiveness of AI in routine clinical use, handling 208,891 AI-driven exams.
- Study included 579,583 exams in total, with 208,891 processed by the AI workflow.
- Addressed the unique US screening paradigm: digital breast tomosynthesis (DBT), single-reading, annual cadence.
- Zero adverse events were reported during the study period, indicating safe deployment.
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Estimated Annual Savings
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Annual Hours Reclaimed
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