DIGITAL ONCOLOGY 5.0: THE CONVERGENCE OF ARTIFICIAL INTELLIGENCE, DIGITAL TWINS, AND PRECISION MEDICINE

Abstract

Digital Oncology 5.0 represents the next evolutionary stage of cancer medicine, characterized by the convergence of artificial intelligence (AI), digital twins, multimodal foundation models, precision medicine, and intelligent healthcare ecosystems capable of delivering predictive, preventive, personalized, participatory, and continuously adaptive cancer care. Conventional oncology frequently relies on fragmented diagnostic workflows, episodic clinical assessments, and isolated interpretation of imaging, pathology, molecular diagnostics, and clinical information, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational frameworks. These intelligent 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, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen Digital Oncology 5.0 by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite substantial technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, explainability, clinical validation, and equitable implementation. This review provides a comprehensive overview of Digital Oncology 5.0, emphasizing the convergence of artificial intelligence, digital twins, and precision medicine as a transformative paradigm for next-generation personalized cancer care.