Foundation AI Models for Predictive Cancer Therapeutics: From Biomarker Discovery to Clinical Translation

Abstract

The rapid evolution of artificial intelligence (AI) has fundamentally transformed precision oncology by enabling predictive computational models that integrate heterogeneous biomedical data for individualized therapeutic decision-making. Among recent technological advances, foundation AI models have emerged as powerful computational frameworks capable of learning generalized biomedical representations from large-scale multimodal datasets and subsequently adapting to numerous downstream oncology applications. Unlike conventional machine learning algorithms trained for isolated predictive tasks, foundation models leverage self-supervised learning, transformer architectures, multimodal representation learning, graph neural networks, and generative AI to integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, spatial biology, laboratory biomarkers, electronic health records, and longitudinal clinical outcomes into unified patient-centered computational representations.These intelligent systems are revolutionizing biomarker discovery, therapeutic response prediction, drug target identification, immunotherapy optimization, adaptive treatment planning, digital twins, and clinical decision support while accelerating translational oncology research. Recent developments in large language models, retrieval-augmented generation, reinforcement learning, federated learning, agentic AI, and explainable artificial intelligence have further expanded the capabilities of predictive cancer therapeutics. Despite remarkable progress, significant challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of foundation AI models for predictive cancer therapeutics, emphasizing computational principles, biomarker discovery, clinical applications, translational challenges, and future perspectives for advancing precision oncology.[1]