Real-World AI-Supported Mammography Screening Detects 17.6% More Cancers Without Increasing Recall Rates
In the PRAIM study -- a nationwide German implementation study involving over 460,000 women -- AI-supported double reading by radiologists achieved a breast cancer detection rate of 6.7 per 1,000, which was 17.6% higher than the 5.7 per 1,000 achieved without AI. Importantly, the recall rate did not increase, and the positive predictive value improved from 14.9% to 17.9%. This is among the largest prospective real-world evaluations demonstrating that AI can meaningfully improve population-level cancer screening.
The original study
Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.
- Authors
- Eisemann N, Bunk S, Mukama T, Baltus H, Elsner SA, Gomille T, et al.
- Journal
- Nature medicine
- Type
- Equivalence Trial, Journal Article, Multicenter Study, Observational Study
- PMID
- 39775040
Original abstract
Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an observational, multicenter, real-world, noninferiority, implementation study comparing the performance of AI-supported double reading to standard double reading (without AI) among women (50-69 years old) undergoing organized mammography screening at 12 sites in Germany. Radiologists in this study voluntarily chose whether to use the AI system. From July 2021 to February 2023, a total of 463,094 women were screened (260,739 with AI support) by 119 radiologists. Radiologists in the AI-supported screening group achieved a breast cancer detection rate of 6.7 per 1,000, which was 17.6% (95% confidence interval: +5.7%, +30.8%) higher than and statistically superior to the rate (5.7 per 1,000) achieved in the control group. The recall rate in the AI group was 37.4 per 1,000, which was lower than and noninferior to that (38.3 per 1,000) in the control group (percentage difference: -2.5% (-6.5%, +1.7%)). The positive predictive value (PPV) of recall was 17.9% in the AI group compared to 14.9% in the control group. The PPV of biopsy was 64.5% in the AI group versus 59.2% in the control group. Compared to standard double reading, AI-supported double reading was associated with a higher breast cancer detection rate without negatively affecting the recall rate, strongly indicating that AI can improve mammography screening metrics.