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.