AI-Based Early Detection of Breast Cancer: Technical, Ethical, and Legal Perspectives

Breast cancer is one of the most prevalent and life-threatening cancers affecting women globally, with survival outcomes highly dependent on early detection. Conventional diagnostic techniques such as mammography, ultrasound, and biopsy are effective but face limitations including human interpretation variability, delayed diagnosis, and limited accessibility in resource-constrained regions. Artificial Intelligence (AI) has emerged as a powerful tool for early breast cancer detection by enabling automated medical image analysis, predictive modeling, and clinical decision support. This paper presents a comprehensive review of AI-based approaches for early detection of breast cancer, covering machine learning and deep learning techniques applied to medical imaging, histopathology, and patient data. In addition to technical advancements, the paper emphasizes ethical challenges and legal considerations such as data privacy, regulatory approval, accountability, and algorithmic fairness. Integrating AI responsibly within legal and regulatory frameworks is essential for ensuring safe, accurate, and equitable deployment in real-world healthcare systems.