Breast imaging has several modalities, each unique in terms of its imaging position, evaluation index, and imaging method. Breast diagnosis is made by combining a large number of past imaging features with the clinical course and histological findings. Artificial intelligence (AI), which extracts the features from image data and evaluates them based on comprehensive analysis, has been making rapid progress in this regard. Many previous studies have demonstrated the usefulness and development potential of AI, such as machine learning and deep learning, in breast imaging. However, despite studies showing the good performance of AI models, their overall utilization remains low, since a large amount of diverse imaging data is required, and prospective verification is necessary to prove its high reproducibility and robustness. Sharing information and collaborating with multiple institutions to collect and verify images of different conditions and backgrounds are vital. If image diagnosis using AI can indeed ensure a more detailed diagnosis, such as breast cancer subtypes or prognosis, it can help develop personalized medicine, which is urgently required. The positive results of AI research, using such image information, can make each modality more valuable than ever. The current review summarized the results of previous studies using AI in each evaluation field and discussed the related future prospects.