  It is imperative that breast cancer is detected precisely and timely to
improve patient outcomes. Diagnostic methodologies have traditionally relied on
unimodal approaches; however, medical data analytics is integrating diverse
data sources beyond conventional imaging. Using multi-modal techniques,
integrating both image and non-image data, marks a transformative advancement
in breast cancer diagnosis. The purpose of this review is to explore the
burgeoning field of multimodal techniques, particularly the fusion of
histopathology images with non-image data. Further, Explainable AI (XAI) will
be used to elucidate the decision-making processes of complex algorithms,
emphasizing the necessity of explainability in diagnostic processes. This
review utilizes multi-modal data and emphasizes explainability to enhance
diagnostic accuracy, clinician confidence, and patient engagement, ultimately
fostering more personalized treatment strategies for breast cancer, while also
identifying research gaps in multi-modality and explainability, guiding future
studies, and contributing to the strategic direction of the field.
