Evidence

RSNA: Podium Presentation
McCabe et al. 2025

Interpretive performance in screening mammograms (SMGs) varies widely. An AI-driven workflow was deployed at scale, and performance before and after implementation was compared between radiologists with and without fellowship training in breast or women’s imaging. The Multistage AI-Driven workflow significantly improved CDR and PPV1 for General radiologists to levels comparable with Fellowship-trained radiologists.

RSNA: Poster Presentation
Louis et al. 2025

This study assesses the cancer subtypes detected in a large-scale deployment of a multistage AI-driven workflow compared to the standard of care (SOC). Employing the multistage AI-driven workflow significantly improved clinical outcomes in terms of CDR in a cohort of over 2400 cancers, with the majority of cancers detected being clinically relevant and no corresponding increase in the proportion of DCIS, showing benefits for screening mammography.

BTOG: Poster Presentation
Palmer et al. 2025

Clinical outcomes from the SWAG TLHC programme were reviewed to better understand potential harms, including over-investigation, missed diagnoses and cancers developing between screening rounds. Of the scans reviewed, 4098/4198 (97.6%) were negative (confirmed true negatives) and 80/4198 (1.9%) were abnormal (confirmed true positives). One person was diagnosed with lung cancer 18 months after a reportedly normal TLC scan, giving a false negative rate of 0.02%.

RSNA: Poster Presentation
Kim et al. 2024

An AI-driven safeguard review process was implemented prospectively, and its custom-built AI algorithm was used to flag the most suspicious screening DBT exams that had not been recalled by the initial interpreting radiologist. An expert breast imaging specialist performed a second, safeguard review of the 2,296 flagged exams. This resulted in the detection of 41 additional cancers, mostly invasive, 22.0% of which were deemed aggressive.