LARGE LANGUAGE MODELS IN PRECISION ONCOLOGY: TRANSFORMING CLINICAL DECISION SUPPORT AND CANCER CARE

Abstract

Large language models (LLMs) are rapidly transforming precision oncology by enabling intelligent clinical reasoning, biomedical knowledge synthesis, multimodal data interpretation, and personalized clinical decision support across the cancer care continuum. Unlike conventional artificial intelligence (AI) systems designed for isolated predictive tasks, LLMs integrate natural language understanding with multimodal foundation models capable of processing radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, electronic health records, biomedical literature, clinical guidelines, and longitudinal patient data. Recent advances in transformer architectures, self-supervised learning, retrieval-augmented generation, graph neural networks, reinforcement learning, and generative AI have enabled comprehensive biomedical reasoning supporting cancer diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and multidisciplinary clinical decision-making. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital twins further enhance precision oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite substantial technological advances, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding hallucination, interpretability, multimodal integration, computational scalability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of large language models in precision oncology, emphasizing their transformative role in clinical decision support and personalized cancer care.