Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects

Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches utilized for classification, segmentation, and detection, emphasize notable findings, and describe large language models for breast cancer diagnosis. Additionally, the editorial will rigorously examine the existing limitations, significant obstacles, and ethical implications related to the extensive implementation of AI in clinical practice. Ultimately, it will focus on the future, delineating emerging patterns and the progression of AI in influencing the next generation of precision medicine for breast cancer patients.