Abstract

Breast cancer stands as a pressing health concern for women across the globe with millions of new cases worldwide each year. The diagnostic procedures often rely on expert analysis and interpretation, which can be subject to errors and limitations. This review offers an in-depth examination of the potential role of artificial intelligence as a transformative tool in the diagnosis of breast cancer. The focus is on the recent advancements within machine learning and deep learning, subsets of artificial intelligence that have shown promise in enhancing the accuracy and efficiency of breast cancer detection and classification. The paper discusses studies published in 2023, which have utilized artificial intelligence models with diagnostic medical datasets to identify, classify and predict the presence of breast cancer with increased precision. By exploring a multitude of approaches, such as federated learning, hybrid deep and machine learning models, and optimization algorithms applied to classification and predictive tasks, this review encapsulates the current state of machine learning and deep learning applications in breast cancer diagnosis.