Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support

Bone tumors (BTs)—including osteosarcoma, Ewing sarcoma, and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location, histological subtype, and molecular alterations. Recent advances in artificial intelligence (AI), particularly deep learning, have enabled the integration of diverse clinical data modalities to support diagnosis, treatment planning, and prognostication in bone oncology. This review provides a comprehensive synthesis of AI-driven MF strategies that incorporate radiological imaging, digital pathology, multi-omics profiling, and electronic health records. We conducted a structured review of peer-reviewed literature published between 2015 and early 2025, focusing on the development, validation, and clinical applicability of AI models for BT diagnosis, subtyping, treatment response prediction, and recurrence monitoring. Although multimodal models have demonstrated advantages over unimodal approaches—especially in handling missing data and improving generalizability—most remain constrained by single-center study designs, small sample sizes, and limited prospective or external validation. Persistent technical and translational challenges include semantic misalignment across modalities, incomplete datasets, limited model interpretability, and regulatory and infrastructural barriers to clinical integration. To address these limitations, we highlight emerging directions such as contrastive representation learning, generative data augmentation, transformer-based fusion architectures, and privacy-preserving federated learning. We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability. In summary, multimodal AI represents a promising paradigm for advancing precision care in BTs. Realizing its full clinical potential will require methodologically rigorous, biologically informed, and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.