Large language models (LLMs) have emerged as transformative tools in medicine, with strong capabilities in language understanding, reasoning, and structured information extraction. Radiation oncology is particularly well suited for LLM integration due to its data-intensive workflows, reliance on structured guidelines, and documentation burden. This review summarizes recent applications, including domain-specific fine-tuning for decision support, automated nomenclature standardization, registry curation using autonomous LLM agents, and protocol-aware radiotherapy plan evaluation using modular retrieval-augmented generation (RAG). Additional applications include patient safety analysis through incident classification and root cause analysis, electronic health record (EHR)-integrated communication, CT simulation order summarization, daily readiness briefings, and patient education systems. Emerging multimodal approaches enable context-aware contouring, while early studies show LLMs can assist treatment planning by interpreting dosimetric feedback. Together, these advances highlight a shift toward clinically grounded, auditable, and workflow-integrated AI systems that enhance efficiency, safety, and patient engagement.
@article{arxiv.2604.03509,
title = {Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence},
author = {Yuzhen Ding and Jason Holmes and Yuexing Hao and Zhengliang Liu and Peilong Wang and Junjie Cui and Meiyun Cao and Caiwen Jiang and Shuoyang Wei and Lin Zhao and Chenbin Liu and Lian Zhang and Yunze Yang and Tianming Liu and Wei Liu},
journal= {arXiv preprint arXiv:2604.03509},
year = {2026}
}