THE NEXT GENERATION OF COMPUTATIONAL ONCOLOGY: EXPLAINABLE, MULTIMODAL, AND FEDERATED ARTIFICIAL INTELLIGENCE

Abstract

Computational oncology is undergoing a profound transformation driven by the convergence of explainable artificial intelligence (XAI), multimodal foundation models, federated learning, and precision medicine. Conventional machine learning approaches have substantially improved cancer diagnosis, prognostic prediction, and therapeutic planning but often remain constrained by limited interpretability, fragmented analysis of heterogeneous biomedical information, and restricted generalizability across healthcare systems. The next generation of computational oncology addresses these limitations through intelligent AI ecosystems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific computational representations. Explainable AI enhances transparency and clinician trust, multimodal learning enables systems-level biological understanding, and federated learning facilitates collaborative model development while preserving patient privacy. Together with graph neural networks, multimodal transformers, self-supervised learning, digital twins, reinforcement learning, multimodal large language models, retrieval-augmented generation, and agentic AI, these technologies establish intelligent computational ecosystems capable of supporting precision diagnosis, biomarker discovery, therapeutic optimization, adaptive disease monitoring, and evidence-based clinical decision support. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of the next generation of computational oncology, emphasizing explainable, multimodal, and federated artificial intelligence as the computational foundation of future precision cancer medicine.