Generative artificial intelligence in medical oncology: A clinically focused narrative review of large language,...

Rapid advances in cancer genetics and genomics, pathophysiology, diagnostics, and therapeutics have substantially increased the complexity of clinical oncology. Integrating large and diverse data streams into timely, patient-specific decisions provides a compelling rationale for incorporating artificial intelligence (AI) into cancer care. Generative artificial intelligence (GenAI) encompasses several classes of models capable of generating new content. This clinically focused review concentrates on GenAI applications based primarily on large language models (LLMs) and vision-language models (VLMs), as well as emerging agentic systems that use these models to perform coordinated, multistep tasks. LLMs demonstrate substantial medical knowledge, strong performance on oncology-related assessments, and emerging capabilities in clinical reasoning. Guideline- and retrieval-augmented systems can improve concordance with expert recommendations and interpretation of complex treatment algorithms. LLMs and VLMs have been applied to documentation, patient communication, information extraction and summarization, clinical decision support, and multimodal data synthesis. However, performance varies substantially across tasks and clinical domains, with important limitations including incomplete or outdated information, training-data bias, automation bias, and confabulation. Agentic AI represents an emerging extension of these technologies in which LLMs or VLMs are incorporated into systems capable of multistep planning, external information retrieval, specialized tool use, and iterative evaluation and refinement. Early oncology applications include evidence synthesis, multimodal clinical decision support, radiology, pathology, surgery, and individualized treatment planning. With rigorous validation, transparent implementation, continual updating, and appropriate expert oversight, GenAI may augment oncologists' judgment, improve patient understanding and experience, streamline cancer care delivery, and advance personalized oncology.