Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

Breast cancer is the leading cause of death from cancer among women globally. According to the World Health Organization (WHO), early detection and treatment can significantly reduce surgeries and improve survival rates. Since deep learning emerged in 2012, it has garnered significant research interest in breast cancer, particularly for diagnosis, treatment, prognosis, and survival prediction. This review specifically focuses on the application of deep learning to breast image analysis (MRI, mammogram, and ultrasound) with a particular emphasis on radiologist involvement in the evaluation process. Studies published between 2019 and 2024 in the Scopus database will be reviewed. We further explore radiologists’ perspectives on the clinical readiness of artificial intelligence (AI) for breast image analysis. By analyzing insights from published articles, we will discuss the challenges, limitations, and future directions for this evolving field. While the review highlights the promise of deep learning in breast image analysis, it also acknowledges critical issues that must be addressed before widespread clinical integration can be achieved.