标题： 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 显示英文标题

标题： Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support

摘要： 由于大型语言模型（LLMs）在决策支持工具中的快速整合，大规模系统正在经历重大变革。 与其他医学领域一样，GPT-4等大型语言模型在放射肿瘤学中的应用也日益受到关注。 通过一项专门针对放射肿瘤学物理这一高度专业主题的100道题考试，尝试评估GPT-4的表现，结果显示GPT-4优于其他大型语言模型。 GPT-4在更广泛的临床放射肿瘤学领域的表现也通过美国放射学会（ACR）的放射肿瘤学住院医师培训（TXIT）考试进行了基准测试，GPT-4取得了74.57%的高准确率。 其根据AAPM TG-263报告重新标记结构名称的表现也进行了基准测试，准确率超过96%。 这些研究揭示了大型语言模型在放射肿瘤学中的潜力。 随着对大型语言模型在一般医疗应用中的潜力和限制的兴趣持续上升5，大型语言模型在放射肿瘤学决策支持中的能力和局限性尚未得到充分探索。 显示英文摘要

摘要： 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.