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Abstract
Breast cancer (BC) remains the most common malignancy among women worldwide. One of the key priorities in combating this disease is early tumor detection, accurate interpretation, and identification of factors guiding optimal treatment selection. Current healthcare realities — including limited human resources, existing regulatory frameworks, and the need to process vast amounts of data — necessitate the development of novel, effective diagnostic tools for BC. In this context, the integration of high-performance technologies based on artificial intelligence (AI) into breast imaging is of paramount importance and relevance. Growing interest in this topic is reflected in the steadily increasing number of scientific publications, which has quadrupled over the past six years. However, the lack of widespread implementation of research findings into routine clinical practice underscores the need to assess the current state of the field, evaluate scientific output, and identify future prospects. This study aims to structure and systematize data from the scientific literature on the application of AI-based mathematical models for the analysis of conventional and multimodal breast imaging in determining the molecular biological subtype of BC, assessing tumor response to drug therapy, and addressing other diagnostic aspects that support a personalized approach to prognosis, treatment planning, and patient management. The most widely used and promising neural network models and training algorithms employed in recent years were analyzed. Their potential for developing unified approaches to integrating AI into the automation of multimodal BC diagnostics was evaluated. The findings enabled the identification of key trends in ongoing research and provided an indirect assessment of their potential effectiveness in predicting diagnostic and treatment scenarios for BC.
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