A Systematic Literature Review on Artificial Intelligence Applications for Breast Cancer Classification

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, needing to be diagnosed early and properly classified for effective treatment. Advancements in artificial intelligence (AI), deep learning, and machine learning techniques have shown great potential in automating breast cancer diagnosis and molecular subtyping using medical imaging. This systematic literature review explores the application of AI in breast cancer classification, focusing on mammographic imaging and its application in distinguishing molecular subtypes. The study follows the PRISMA guideline, investigating studies from multiple digital libraries published between 2020 and November 2024. Findings show that while deep learning models have significantly improved breast cancer detection, challenges remain in optimizing classification models for molecular subtypes, balancing accuracy and interpretability, and integrating AI-based tools into clinical practice workflows. Besides, heterogeneity in preprocessing pipeline algorithms and dataset limitations highlights the importance of conducting additional research to develop robust and generalized classification models. This review underscores the importance of AI-driven solutions in advancing breast cancer diagnosis and treatment planning while providing insights into future research directions.