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Related Experiment Video

Updated: May 28, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

AI as a Safety Net Reader for Mammograms Classified as Normal or Benign in the French Screening Program.

Christophe Tourasse¹, Benoît Mesurolle², Maud Ottavy³

¹Centre d'imagerie médicale, Imagerie Hôpital Privé Mermoz, 55 Avenue Jean Mermoz, 69008 Lyon, France.

Radiology. Artificial Intelligence

|May 27, 2026

Summary

Artificial intelligence (AI) can identify low-risk mammograms in the French breast cancer screening program, potentially reducing radiologist workload by 77%. This AI triage could safely bypass second readings for many negative screening mammograms, focusing expert review on higher-risk cases.

Keywords:

Breast Computer Aided Diagnosis (CAD)Mammography

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

The French national breast cancer screening program uniquely employs second reading only for mammograms initially classified as negative (BI-RADS 1-2).
This retrospective study investigated the potential of artificial intelligence (AI) to identify a subgroup of these negative mammograms that could safely omit second reading.

Purpose of the Study:

To assess if AI can identify negative screening mammograms that could bypass second reading in the French national program.
To evaluate the impact of AI-driven triage on radiologist workload and cancer detection rates.

Main Methods:

Analysis of 55,589 screening mammograms from women aged 50-74 (2015-2019) initially classified as BI-RADS 1-2.
Comparison of second-reading outcomes with a commercial AI system's risk classification (threshold ≥ 5).
Evaluation of cancer detection rates and interval cancer rates between AI-classified risk groups.

Main Results:

AI classified 76.6% of mammograms as low risk (≤ 4) and 23.3% as non-low risk (≥ 5).
Only one cancer (0.002%) was detected in the AI-low risk group, compared to 11 cancers (0.020%) in the AI-non-low risk group (P < .001).
Excluding AI-low risk mammograms could reduce second-reading workload by approximately 77%.

Conclusions:

AI triage shows potential to significantly reduce workload in the French breast cancer screening program by identifying low-risk mammograms.
A small but measurable risk of missed cancers exists, necessitating careful governance and prospective validation.
AI could optimize radiologist focus on higher-risk cases, improving screening efficiency.