MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE FOR PRECISION ONCOLOGY: FROM CANCER DIAGNOSIS TO THERAPEUTIC DISCOVERY

Abstract

Multimodal generative artificial intelligence (AI) is redefining precision oncology by enabling intelligent computational systems capable of synthesizing heterogeneous biomedical information, generating clinically actionable knowledge, and supporting personalized cancer diagnosis, prognostic prediction, therapeutic optimization, and drug discovery. Unlike conventional artificial intelligence models designed for isolated prediction tasks, multimodal generative AI integrates radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified generative foundation models capable of multimodal reasoning and knowledge generation. Recent advances in transformer architectures, self-supervised learning, graph neural networks, diffusion models, variationalautoencoders, generative adversarial networks, retrieval-augmented generation, and multimodal large language models have enabled comprehensive biomedical representation learning supporting cancer diagnosis, biomarker discovery, molecular characterization, immunotherapy selection, digital twin simulation, therapeutic optimization, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including federated learning, explainable artificial intelligence, reinforcement learning, and agentic AI further strengthen multimodal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, model interpretability, computational scalability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal generative artificial intelligence in precision oncology, emphasizing its transformative role from cancer diagnosis to therapeutic discovery.