Artificial Intelligence in the Management of Breast Cancer: A Comprehensive Review

Abstract

Artificial intelligence (AI) has rapidly emerged as a transformative tool in modern medicine, particularly in oncology, where it has shown significant potential in improving diagnostic accuracy and early detection of cancer. Breast cancer remains one of the most prevalent malignancies worldwide, and early diagnosis is crucial for reducing mortality. This review provides a comprehensive overview of the current applications of AI in cancer diagnosis, with a specific focus on breast cancer. A narrative review of recent literature was conducted using major databases such as PubMed and Scopus, including studies on machine learning and deep learning techniques applied to imaging, histopathology, and clinical decision-making. AI-based models, particularly convolutional neural networks, have demonstrated high accuracy in analyzing mammographic images and detecting early-stage breast cancer, with some studies showing performance comparable to, or exceeding, that of experienced radiologists. Furthermore, AI has contributed to improved lesion classification, reduced false-positive rates, and enhanced diagnostic efficiency. In histopathology, AI systems have also shown strong capabilities in tumor detection and grading. Despite these promising advancements, several challenges remain, including data bias, lack of standardization, ethical concerns, and limited integration into clinical practice. Overall, AI represents a promising approach for improving breast cancer diagnosis, although further large-scale validation and clinical implementation are needed.