Diagnostic Test Accuracy of AI-Assisted Mammography for Breast Imaging: A Narrative Review

Abstract

<p>This narrative review delves into the diagnostic test accuracy of AI-assisted mammography for breast imaging, examining its potential, challenges, and future implications. AI's integration into medical imaging has promised to revolutionize breast cancer diagnosis, prompting an evaluation of its performance and impact. A comprehensive literature search was conducted, resulting in the identification of key articles. These studies encompassed diverse AI techniques, training datasets, and geographical contexts. The review synthesizes findings on sensitivity, specificity, interpretability, ethical considerations, and global implications. The studies collectively demonstrate AI's potential in enhancing diagnostic accuracy, with deep learning algorithms exhibiting commendable sensitivity and specificity levels. However, challenges arise in data diversity, algorithmic bias, and interpretability. Ethical and regulatory considerations underscore the need for responsible AI deployment. The concept of augmented intelligence, wherein AI collaborates with radiologists, holds promise for future clinical practice. AI-assisted mammography holds transformative potential in breast cancer diagnosis, accuracy and efficiency. While challenges exist, collaborative efforts between stakeholders are essential to navigate complexities and realize AI's benefits. The review concludes that while AI holds promise in breast cancer diagnosis, ethical, global, and research considerations are imperative for its responsible and effective deployment.</p>