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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Adding artificial intelligence case malignancy scoring to reduce screen-reading workload in breast screening program:

Andrea Nitrosi¹, Paolo Giorgi Rossi², Laura Verzellesi^3,4

¹Medical Physics Unit - Oncology and Innovative Technologies Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

La Radiologia Medica

|November 26, 2025

Summary

Integrating AI case malignancy score (AI-CMS) into mammography screening reduces radiologist workload by up to 36.1% while maintaining cancer detection rates. This AI-supported strategy also lowers recall and false positive rates, proving economically sustainable.

Keywords:

Artificial intelligence Business CAD Mammography Non-inferiority Retrospective Screening

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Radiology

Background:

Mammography screening is crucial for early breast cancer detection.
Radiologist workload can impact efficiency and diagnostic accuracy.
AI algorithms can provide confidence scores for malignancy assessment.

Purpose of the Study:

To evaluate a strategy integrating AI case malignancy score (AI-CMS) into mammography screening.
To assess the impact of AI-CMS on radiologist workload and screening performance.
To determine the economic sustainability of AI-CMS integration.

Main Methods:

Retrospective analysis of 89,176 screening mammograms.
Simulation of a strategy using AI-CMS to guide radiologist recall decisions.
Evaluation of AI-CMS thresholds (5-25%) on recall rate, cancer detection, and workload.

Main Results:

AI-CMS integration reduced human workload by 13.4% to 36.1%.
Recall rate decreased to 4.0-4.3%, and false positive rate decreased from 3.9% to 3.5-3.8%.
Positive predictive value increased to 12.6-13.3%, and no cancers were missed at 5% threshold.

Conclusions:

AI-CMS support can significantly decrease radiologist workload in mammography screening.
The strategy shows potential for reducing unnecessary recalls without compromising cancer detection.
The AI-CMS integration approach is economically viable and sustainable.