# Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications

## TL;DR

This review examines how radiation risks from mammography are modeled and how AI might improve personalized risk assessments.

## Contribution

The paper provides a scoping review of radiation risk modeling and AI applications in mammography screening.

## Key findings

- Breast density and imaging parameters significantly affect radiation dose.
- AI shows promise for improving individualized dose-risk assessments.
- Current models have inconsistencies and vendor-specific limitations.

## Abstract

Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain a potential barrier to participation. This scoping review explores existing models estimating long‐term radiation risks associated with repeated mammography screening. A structured search across five databases (Medline, Embase, Scopus, Web of Science and CINAHL) along with manual searching identified 24 studies published between 2014 and 2024. These were categorised into three themes: (1) models estimating dose–risk profiles, (2) factors affecting radiation dose and (3) the use of artificial intelligence (AI) in dose estimation and mammographic breast density (MBD) estimation. Studies showed that breast density, compressed breast thickness (CBT) and technical imaging parameters significantly influence mean glandular dose (MGD). Modelling studies highlighted the low risk of radiation‐induced cancer, inconsistencies in protocols and vendor‐specific limitations. AI applications are emerging as promising tools for improving individualised dose–risk assessments but require further development for compatibility across different imaging platforms.

This scoping review examined models estimating long‐term radiation risks from repeated mammography screening. While modelling indicates the risks are low, approaches vary by protocol and vendor. Emerging AI tools show promise for improving personalised risk estimation, though further development is needed for cross‐platform compatibility.