ARTIFICIAL INTELLIGENCE–DRIVEN CANCER KNOWLEDGE GRAPHS FOR PRECISION DIAGNOSIS AND THERAPEUTIC DISCOVERY

Abstract

Artificial intelligence (AI)-driven cancer knowledge graphs are emerging as a transformative computational paradigm for precision oncology by integrating heterogeneous biomedical information into structured, interconnected representations of cancer biology. Conventional oncology workflows frequently analyze radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, laboratory investigations, biomedical literature, electronic health records, and clinical guidelines independently, limiting comprehensive understanding of molecular mechanisms and therapeutic opportunities. Recent advances in foundation AI models, graph neural networks, multimodal transformer architectures, self-supervised learning, reinforcement learning, retrieval-augmented generation, and generative artificial intelligence have enabled construction of dynamic cancer knowledge graphs capable of integrating genes, proteins, signaling pathways, biomarkers, imaging phenotypes, histopathological characteristics, therapeutic agents, clinical trials, digital biomarkers, patient outcomes, and biomedical evidence into unified computational ecosystems. These intelligent systems support precision diagnosis, biomarker discovery, molecular pathway analysis, drug target identification, therapeutic optimization, drug repurposing, adaptive clinical trials, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital health ecosystems further strengthen cancer knowledge graphs by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding knowledge harmonization, computational scalability, interoperability, data quality, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of AI-driven cancer knowledge graphs, emphasizing their role in precision diagnosis and therapeutic discovery for next-generation oncology.