Breast cancer is known to be an extremely dangerous disease, which if diagnosed on time has a very high survival rate. The incidence and mortality rate from breast cancer are increasing, so in order to reduce these numbers, new technological solutions are being sought that will enable the earliest possible detection of breast cancer. Although the original solution was seen in traditional computer-adied detection systems (CAD) applied to various radiological methods of breast imaging, the results of the variety of studies discussed in this paper found that they did not meet their original expectations in breast cancer diagnosis. The use of conventional CAD systems still had too many limitations such as a decrease in specificity and positive predictive value with an increase in false positive findings and an increase in the recall rate. However, the development of algorithms based on artificial intelligence (AI) has improved the quality and accuracy of conventional CAD systems. Unlike conventional CAD system that are based on hand-crafted features, depth learning, as a subfield of AI, is based on representational learning. In representational learning, the algorithm itself during training determines the features in the image that indicate the presence of a lesion. Recently, such deep learning algorithms have been applied to digital mammography (FFDM), digital breast tomosynthesis (DBT), and magnetic resonance imaging (MRI). In this paper, the analysis of various studies discusses the possibilities, but also the limitations of new AI-based applications for different breast imaging modalities. Due to the small number of studies conducted on the topic of AI systems and the need for an extremely large set of data for training and validation of the algorithm, many scientists continue to doubt this new method. Despite the emergence of these limitations, the AI approach has the ability to detect useful features in the image that are still invisible to the human eye. Future advances in technology will significantly improve AI systems and their implementation in health systems will be inevitable.