Generative Artificial Intelligence in Oncology: From Clinical Documentation to Precision Therapeutic Discovery

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The rapid growth of biomedical data generated from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory investigations, wearable technologies, and electronic health records has created unprecedented opportunities for artificial intelligence (AI)-driven oncology. Among recent innovations, generative artificial intelligence (GenAI) has emerged as a transformative technology capable of generating text, images, molecular structures, synthetic biomedical data, computational models, and clinical knowledge that support nearly every stage of cancer care. Unlike conventional discriminative AI models that primarily classify or predict outcomes, generative AI synthesizes new information by learning complex probability distributions from large multimodal datasets. Advances in transformer architectures, large language models (LLMs), diffusion models, multimodal foundation models, retrieval-augmented generation (RAG), graph neural networks, reinforcement learning, and agentic AI have significantly expanded the role of GenAI in oncology. These intelligent systems are increasingly applied to clinical documentation, medical imaging, digital pathology, biomarker discovery, molecular design, drug development, clinical trial optimization, patient communication, clinical decision support, and precision therapeutics. Despite remarkable progress, important challenges remain regarding hallucination, explainability, regulatory validation, cybersecurity, ethical governance, data privacy, algorithmic bias, and responsible clinical implementation. This review provides a comprehensive overview of generative artificial intelligence in oncology, emphasizing computational foundations, current clinical applications, implementation challenges, and future perspectives for precision cancer medicine.[1]