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Early Results of Using AI in Mammography Screening for Breast Cancer
Hadar Sandler Rahat1,2, Tal Friehmann1,2, Marva Dahan Shemesh1,2
1Department of Imaging, Rabin Medical Center-Beilinson Hospital, Petach Tikva 4941492, Israel.
Journal of Clinical Medicine
|November 13, 2025
Summary
Artificial intelligence (AI) integration in mammography significantly improved cancer detection rates and reduced false negatives in a real-world screening program. This AI advancement enhances screening efficacy and accuracy for breast cancer detection.
Area of Science:
- Radiology
- Artificial Intelligence in Medicine
- Breast Cancer Screening
Background:
- Advancements in Artificial Intelligence (AI) offer potential solutions for mammographic screening challenges.
- AI can enhance Computer-Aided Detection (CAD) systems, improving accuracy and reducing false positives.
- Previous AI studies primarily used control trials with cancer-enriched datasets.
Purpose of the Study:
- To evaluate the real-world impact of integrating an AI system into a breast cancer screening program.
- To assess changes in key screening metrics following AI implementation.
Main Methods:
- An AI system (iCAD version 2.0) was integrated into the mammographic screening protocol in January 2021.
- Audit data from 31,176 mammograms (24,373 pre-AI, 6803 post-AI) between 2017-2021 were analyzed.
- Logistic regression analysis was used to determine statistical significance (p < 0.05).
Main Results:
- Cancer detection rate increased significantly from 6.2 to 9.3 per 1000 women (2019-2021).
- Stage 1 cancer detection reached 100%, and the false negative rate dropped to 0%.
- Detection of ductal carcinoma in situ (DCIS) decreased from 36.4% to 20%.
Conclusions:
- AI integration in mammographic screening shows promising results for improving cancer detection rates.
- AI significantly reduces false negative rates, enhancing overall screening efficacy.
- AI demonstrates potential to improve the accuracy and efficiency of breast cancer screening programs.
