Decoding Cancer: AI-Driven Insights in Breast Cancer Detection and Care

Authors: Priyanka Priyadarshini Pattnaik, Amiya Kumar Prusty

DOI: 10.18231/j.ijcaap.11866.1761801610

Keywords: Artificial Intelligence, Breast Cancer, Deep Learning, Diagnostic Tools, Radiomics, Precision Medicine, Machine Learning, Oncology.

Abstract: The integration of artificial intelligence (AI) into breast cancer research and clinical practice has revolutionized the landscape of oncology by enhancing accuracy, speed, and personalization across the cancer care continuum. This narrative review explores recent advancements in AI applications for breast cancer detection, diagnosis, and treatment, highlighting innovations in imaging, pathology, genomics, and therapeutic decision-making. We discuss how deep learning and machine learning models are improving early detection through enhanced mammographic interpretation and radiomics, as well as their role in histopathological classification and biomarker discovery. Furthermore, we examine the emerging use of AI in predicting treatment response, optimizing radiotherapy planning, and supporting precision medicine through integrative multi-omics approaches. While these technologies promise significant clinical benefit, the review also addresses the limitations, such as algorithmic bias, data privacy concerns, and the need for rigorous clinical validation. Overall, the expanding frontier of AI holds transformative potential in breast cancer care, paving the way for more accurate diagnostics and individualized treatment strategies.