ARTIFICIAL INTELLIGENCE FOR PRECISION CANCER PREVENTION: PREDICTIVE ANALYTICS, RISK STRATIFICATION, AND EARLY INTERVENTION

Abstract

Artificial intelligence (AI) is transforming cancer prevention by enabling precision risk prediction, individualized screening, early disease detection, and personalized preventive interventions through comprehensive integration of multimodal biomedical data. Conventional cancer prevention strategies primarily rely on population-level risk estimates based on age, family history, environmental exposures, and lifestyle factors, often failing to capture the complex biological heterogeneity underlying cancer susceptibility and disease initiation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, spatial biology, circulating tumor DNA, laboratory biomarkers, wearable physiological monitoring, environmental exposures, electronic health records, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer risk. These intelligent systems support individualized risk stratification, biomarker discovery, personalized screening, early cancer detection, behavioral intervention, chemoprevention, lifestyle optimization, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, digital twins, cloud-native healthcare platforms, and digital health ecosystems further strengthen preventive oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of artificial intelligence for precision cancer prevention, emphasizing predictive analytics, risk stratification, and early intervention as transformative approaches for next-generation preventive oncology.