Thanks to the rapidly evolving integration of LLMs into decision-support tools, a significant transformation is happening across large-scale systems. Like other medical fields, the use of LLMs such as GPT-4 is gaining increasing interest in radiation oncology as well. An attempt to assess GPT-4's performance in radiation oncology was made via a dedicated 100-question examination on the highly specialized topic of radiation oncology physics, revealing GPT-4's superiority over other LLMs. GPT-4's performance on a broader field of clinical radiation oncology is further benchmarked by the ACR Radiation Oncology In-Training (TXIT) exam where GPT-4 achieved a high accuracy of 74.57%. Its performance on re-labelling structure names in accordance with the AAPM TG-263 report has also been benchmarked, achieving above 96% accuracies. Such studies shed light on the potential of LLMs in radiation oncology. As interest in the potential and constraints of LLMs in general healthcare applications continues to rise5, the capabilities and limitations of LLMs in radiation oncology decision support have not yet been fully explored.

本研究针对大型语言模型在放射肿瘤学应用中的能力与局限性进行了深入探讨，填补了这一领域的研究空白。通过对GPT-4在放射肿瘤学物理的100道专门题目的评估，发现GPT-4在相关领域表现出显著优越性，这为放射肿瘤学决策支持系统的未来发展提供了重要的见解。

探索大型语言模型在放射肿瘤学决策支持中的能力与局限性