Improving Imaging Outcomes: The Role of Artificial Intelligence in Screening Digital Mammography and Digital Breast Tomosynthesis

Authors

• Christine Boyer MPAP, PA-C, University of LynchburgFollow

Specialty

Surgery

Advisor

Dr. Debra Munsell

Abstract

ABSTRACT

The purpose of this review is to evaluate the role of artificial intelligence (AI) in improving imaging outcomes in screening digital mammography and digital breast tomosynthesis. AI systems using machine learning and deep learning algorithms can analyze large imaging datasets, support radiologists, and enhance cancer detection while reducing workload. A comprehensive search of PubMed and the Cochrane Library identified systematic reviews, meta-analyses, and randomized controlled trials assessing AI in breast cancer screening. Data from three major studies were further examined to evaluate AI’s impact on early cancer detection and diagnostic performance.

Findings indicate that AI used in conjunction with radiologists achieves diagnostic accuracy comparable to, and in some cases exceeding, traditional double-reading. Integration of AI has been associated with a 44.3% reduction in radiologist workload without compromising safety or diagnostic performance. These improvements may streamline workflows, enable radiologists to focus on more complex cases, and accelerate time to diagnosis. Further research is needed to evaluate deep learning applications in digital breast tomosynthesis and to establish regulatory frameworks addressing legal responsibility and implementation standards. Overall, this review supports AI as a valuable tool for enhancing breast cancer screening by improving efficiency, diagnostic accuracy, and healthcare delivery. Continued advancement of AI-driven algorithms holds promise for earlier and more precise detection of breast cancer.

Keywords: artificial Intelligence, machine learning, deep learning, screening digital mammography, digital breast tomosynthesis

Recommended Citation

Boyer C. Improving Imaging Outcomes: The Role of Artificial Intelligence in Screening Digital Mammography and Digital Breast Tomosynthesis. University of Lynchburg DMSc Doctoral Project Assignment Repository. 2026; 8(1).