Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models

Abstract

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT.

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