AI-POWERED COMPUTATIONAL ONCOLOGY: ADVANCING PERSONALIZED CANCER MEDICINE THROUGH MULTIMODAL DATA FUSION

Abstract

Computational oncology is undergoing a transformative evolution through the integration of artificial intelligence (AI), multimodal biomedical data fusion, and precision medicine into intelligent computational ecosystems capable of supporting personalized cancer care throughout the disease continuum. Conventional oncology frequently relies on fragmented interpretation of imaging, pathology, molecular diagnostics, and clinical information, thereby limiting comprehensive understanding of the complex biological mechanisms governing tumor evolution and therapeutic response. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-powered computational oncology, emphasizing multimodal data fusion as a transformative framework for advancing personalized cancer medicine.