TOWARDS GENERALIST ARTIFICIAL INTELLIGENCE IN ONCOLOGY: FOUNDATION MODELS FOR INTEGRATED CANCER DIAGNOSIS AND PERSONALIZED CARE

Abstract

The rapid evolution of artificial intelligence (AI) has accelerated the transition from task-specific algorithms toward generalist foundation models capable of performing diverse clinical, molecular, and imaging tasks using unified computational intelligence. In oncology, these next-generation systems have the potential to transform cancer diagnosis and personalized care by integrating heterogeneous biomedical information into continuously learning computational ecosystems. Conventional oncology frequently depends on fragmented interpretation of radiological imaging, digital pathology, molecular diagnostics, laboratory investigations, and clinical information, limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across 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. These intelligent computational 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 generalist oncology intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important 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 generalist artificial intelligence in oncology, emphasizing foundation models for integrated cancer diagnosis and personalized patient care.