Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based...

In recent years, the emergence of new-generation deep learning-based artificial intelligence (AI) tools has reignited enthusiasm about the potential of computer-assisted detection (CADe) and diagnosis (CADx) for screening mammography. For screening mammography, digital breast tomosynthesis (DBT) combined with acquired digital 2D mammography or synthetic 2D mammography is widely used throughout the United States. As of this writing in July 2024, there are six Food and Drug Administration (FDA)-cleared AI-based CADe/x tools for DBT. These tools detect suspicious lesions on DBT and provide corresponding scores at the lesion and examination levels that reflect likelihood of malignancy. In this article, we review the evidence supporting the use of AI-based CADe/x for DBT. The published literature on this topic consists of multireader, multicase studies, retrospective analyses, and two "real-world" evaluations. These studies suggest that AI-based CADe/x could lead to improvements in sensitivity without compromising specificity and to improvements in efficiency. However, the overall published evidence is limited and includes only two small postimplementation clinical studies. Prospective studies and careful postimplementation clinical evaluation will be necessary to fully understand the impact of AI-based CADe/x on screening DBT outcomes.