Ting-Ruen Wei, Yuling Yan
Traditional artificial intelligence (AI)-based methods for breast cancer diagnosis often rely on a single modality, such as ultrasound images. With the rise of multimodal approaches, multiple data sources, including imaging from diverse medical modalities, structured clinical information, and unstructured medical reports, are increasingly integrated to provide richer and more informative signals for model training. This survey reviews the data modalities employed in AI-based breast cancer research, examines common multimodal combinations and fusion strategies, and discusses their applications across clinical tasks such as diagnosis, treatment planning, and outcome prediction. By consolidating current literature and identifying critical gaps, this survey aims to guide future research toward the development of reliable, clinically relevant multimodal AI systems for use in breast cancer management. • Multimodal AI integrates imaging, clinical data, and reports in breast cancer care. • Survey shows AI improves diagnosis, tumor analysis, and treatment planning. • Diverse data fusion proves vital for advancing AI-driven breast cancer solutions.