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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.
Academic Publication Nationwide real-world implementation of AI for cancer detection in population-based mammography screening
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Nationwide real-world implementation of AI for cancer detection in population-based mammography screening
Abstract Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an observational, multicenter, r...
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What is the core focus of the research titled 'Nationwide real-world implementation of AI for cancer detection in population-based mammography screening'?
This literature focuses on: 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 co...
Are there open-source GitHub repositories related to Nationwide real-world implementation of AI for cancer detection in population-based mammography screening?
Yes, open-source projects like QuipNetwork/xq-rs (A rust implementation of the Quip Network's quantum virtual machine.) are actively building upon these concepts.
Which startups are commercializing the technology behind Nationwide real-world implementation of AI for cancer detection in population-based mammography screening?
Products like Qwen3.6-Plus are bringing this to market. Their focus is: Multimodal AI optimized for real-world coding agents.
What other academic literature is closely related to 'Nationwide real-world implementation of AI for cancer detection in population-based mammography screening'?
Yes, highly correlated activity was mapped. An entry titled 'Nationwide real-world implementation of AI for cancer detection in population-based mammography screening' discusses this: Abstract Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studie...
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GitHubQuipNetwork/xq-rs
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GitHubQuipNetwork/quip-protocol-rs
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