AI-AUGMENTED PRECISION THERAPEUTICS: FOUNDATION MODELS FOR BIOMARKER DISCOVERY AND PERSONALIZED CANCER TREATMENT

Abstract

Artificial intelligence (AI) is transforming precision therapeutics by enabling comprehensive integration of molecular, imaging, pathological, and clinical information into intelligent computational ecosystems capable of supporting biomarker discovery and individualized cancer treatment. Conventional precision oncology frequently depends upon fragmented interpretation of genomic alterations, histopathological findings, radiological imaging, and clinical information, limiting comprehensive understanding of tumor biology and therapeutic responsiveness. 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 evidence-based 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 AI-augmented therapeutics by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. 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 AI-augmented precision therapeutics, emphasizing foundation models for biomarker discovery and personalized cancer treatment as a transformative paradigm for next-generation oncology.