AI-ENABLED PRECISION RADIOPATHOMICS: INTEGRATING IMAGING, HISTOPATHOLOGY, AND GENOMICS FOR PERSONALIZED CANCER CARE

Abstract

Artificial intelligence (AI)-enabled precision radiopathomics has emerged as a transformative paradigm in precision oncology by integrating radiological imaging, digital histopathology, genomics, and multimodal biomedical data into unified computational frameworks for personalized cancer care. Conventional oncology frequently analyzes imaging, pathology, and molecular biomarkers independently, limiting comprehensive understanding of tumor heterogeneity, molecular evolution, therapeutic responsiveness, and disease progression. 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 computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, 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 representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, 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, cancer knowledge graphs, and digital health ecosystems further strengthen radiopathomics 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 AI-enabled precision radiopathomics, emphasizing integration of imaging, histopathology, and genomics for personalized cancer care.