Advancements and Challenges in AI-Driven Breast Cancer Detection: A Comprehensive Review

Breast cancer is the most common cause of death among women worldwide, which may decline with advancements in early detection methods. Healthcare has been transformed by Artificial Intelligence (AI), particularly in breast cancer detection. AI algorithms have improved early-stage cancer detection and reduced diagnostic errors by analyzing imaging data such as mammograms, MRIs, ultrasounds, and genomic information. However, evaluating the findings remains a human role. The most recent improvements regarding AI applications, Machine learning (ML), and Deep learning (DL) for breast cancer screening are reviewed in this paper/study. AI still needs developments in clinical practice to overcome challenges such as data scarcity and model explainability and reduce challenges. The training gained from this review that large, encrypted datasets are necessary for training interpretable AI models and for closer integration with conventional healthcare workflows is discussed. Finally, possible applications of AI are reviewed in precision medical techniques with a focus on improving patient care for personalized detection and treatment of breast cancer. While AI has been enhanced during the last years considering diagnosis related to breast cancer, some issues that remain are under consideration for resolution. Using AI in breast cancer diagnosis and treatment offers hope for better healthcare outcomes.