Artificial Intelligence and Early Breast Cancer Detection

Authors

• Kahley N. Wampler, University of LynchburgFollow

Specialty

General Surgery

Advisor

Sarah Bolander

Abstract

The objective of this review is to evaluate the ability of artificial intelligence (AI) to improve the accuracy of mammographic interpretation and reduce radiologists' screen workload. A comprehensive literature review was conducted using PubMed and IEEE Xplore with the keywords “artificial intelligence,” “mammogram,” and “breast cancer.” Filters were applied to identify meta-analyses, systematic reviews, and randomized controlled trials (RCTs) published between 2020 and 2025. These articles were further evaluated for relevance to AI-assisted mammographic interpretation and for their impact on diagnostic accuracy and radiologist workload, resulting in the selection of three studies that met the inclusion criteria. The findings of these studies suggest that augmenting mammographic interpretation with artificial intelligence improves accuracy and reduces radiologists' screen workload. Selection bias and the use of varying AI models underscore the need for additional research and standardization of AI tools to support the future implementation of AI in mammographic interpretation. This review emphasizes the potential of artificial intelligence to improve patient outcomes, reduce radiologist burnout, and reduce breast cancer mortality through earlier detection.

Recommended Citation

Wampler KN. Artificial Intelligence and Early Breast Cancer Detection. University of Lynchburg DMSc Doctoral Project Assignment Repository. 2026; 8(1).