AI-DRIVEN PRECISION CLINICAL TRIALS: FOUNDATION MODELS FOR ADAPTIVE ONCOLOGY RESEARCH AND PERSONALIZED THERAPEUTICS

Abstract

Artificial intelligence (AI) is fundamentally transforming oncology clinical research through the emergence of foundation models capable of enabling adaptive, data-driven, and patient-centered clinical trials. Conventional oncology trials frequently face substantial challenges including prolonged patient recruitment, heterogeneous populations, limited biomarker stratification, high operational costs, delayed endpoint evaluation, and restricted generalizability. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, 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 frameworks. These intelligent systems support patient identification, biomarker discovery, adaptive trial design, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, real-time safety 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 clinical research platforms, and digital biomarkers further strengthen precision clinical trials by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, regulatory, and implementation 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-driven precision clinical trials, emphasizing foundation models for adaptive oncology research and personalized therapeutics.