Data- and knowledge-driven multimodal learning in computational pathology: A comprehensive survey

Computational pathology (CPath) is undergoing a transformative evolution, driven by the convergence of large-scale biomedical data and breakthroughs in deep learning and large language models. Medical data is collected using different measurement modalities or sources, each providing complementary information. Multi-scale and multi-modal medical data represent information across multiple biological and clinical levels, ranging from molecular measurements to whole-organ and patient-level observations. While most efforts in the field of CPath focused on unimodal analysis of whole slide images (WSIs), the field has expanded toward multimodal integration, combining pathological WSIs with radiology imaging, genomic profiles, and Electronic Health Records to achieve comprehensive and context-aware interpretation of disease phenotypes. In this survey, we synthesize reviews of advances in CPath along two axes: data-driven multimodal learning and knowledge-driven enhancement. We also discuss the fusion strategies across visual and non-visual modalities, cross-modal contrastive learning, and self-supervised pretraining with unannotated data at scale. In parallel, we highlight representative applications that incorporate prior domain knowledge, such as morphological constraints, geometrical representations, and text prompts, into model design and supervision to enhance both generalizability and interpretability. Finally, we examine transformative clinical applications and highlight future directions toward generalizable, explainable, and clinically actionable artificial intelligence for pathology systems. By bridging cutting-edge technical innovation with real-world diagnostic requirements, in conducting this review, we aim to provide a conceptual roadmap for the next generation of trustworthy artificial intelligence in digital pathology.