ABSTRACT
Artificial intelligence (AI) is fundamentally transforming precision oncology by orchestrating heterogeneous biomedical information into intelligent computational ecosystems that support personalized cancer care throughout the clinical continuum. Conventional oncology frequently relies on fragmented analysis of radiological imaging, digital pathology, molecular biomarkers, laboratory investigations, and clinical records, limiting comprehensive understanding of tumour biology and therapeutic response. Recent advances in foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. Digital twin technologies extend these capabilities by generating continuously evolving virtual representations of patients capable of simulating tumour evolution, therapeutic response, treatment-related toxicity, recurrence, and survivorship. Simultaneously, intelligent biomarker discovery and multimodal clinical reasoning provide adaptive decision support for diagnosis, prognostic prediction, precision therapeutics, immunotherapy optimization, and disease monitoring. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen these ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive precision oncology. Despite remarkable technological progress, important 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-orchestrated precision oncology, emphasizing integration of digital twins, biomarkers, and clinical intelligence as a new paradigm for personalized cancer care.[1]
Keywords: Artificial intelligence, Precision oncology, Digital twins, Foundation models, Biomarkers, Clinical intelligence, Multimodal learning, Computational oncology, Personalized medicine, Clinical decision support.
