ARTIFICIAL INTELLIGENCE–DRIVEN CLINICAL ONCOLOGY INTELLIGENCE: FROM EARLY CANCER DETECTION TO PRECISION THERAPEUTICS

Abstract

Artificial intelligence (AI) is transforming modern oncology by enabling the development of intelligent clinical ecosystems capable of supporting the entire continuum of cancer care, from early detection and diagnosis to precision therapeutics and long-term survivorship. Conventional oncology frequently relies on fragmented diagnostic modalities, episodic clinical assessments, and static predictive models that inadequately capture the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration 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 into unified patient-specific 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 intelligent 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 clinical oncology intelligence by enabling collaborative, privacy-preserving, and continuously adaptive biomedical reasoning. 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-driven clinical oncology intelligence, emphasizing its role in advancing early cancer detection, precision therapeutics, and personalized cancer medicine.