Leveraging Artificial Intelligence in Breast Cancer Screening and Diagnosis

Breast cancer remains the most prevalent malignancy worldwide, posing a significant public health burden due to its high incidence and mortality rates. Early detection through mammography has been instrumental in reducing breast cancer-related deaths; however, traditional screening methods are constrained by human limitations, including variability in interpretation and resource-intensive workflows. Artificial intelligence (AI) has emerged as a transformative tool in breast cancer diagnostics, leveraging machine learning (ML) and deep learning (DL) algorithms to enhance accuracy, efficiency, and accessibility. AI applications in digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound, and magnetic resonance imaging (MRI) have demonstrated improved sensitivity and specificity, reducing false positives and false negatives while optimizing radiologist workload. Despite these advancements, challenges such as data accessibility, algorithm biases, regulatory constraints, and clinical integration hinder widespread AI adoption. Addressing these limitations requires standardized validation protocols, enhanced interpretability through explainable AI (XAI), and improved clinician and patient education. This editorial explores the evolving role of AI in breast cancer screening and diagnosis, emphasizing its potential to bridge healthcare disparities and improve global breast cancer outcomes.