Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence

TL;DR

This review discusses how large language models are transforming radiation oncology through workflow automation, decision support, safety analysis, and multimodal clinical applications, improving efficiency and safety.

Contribution

It summarizes recent advancements in applying LLMs to radiation oncology workflows, including fine-tuning, autonomous agents, and multimodal approaches, highlighting a shift toward integrated clinical AI systems.

Findings

LLMs enable automated nomenclature standardization.

LLMs assist in treatment planning by interpreting dosimetric feedback.

Emerging multimodal LLM approaches support context-aware contouring.

Abstract

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.