This paper presents RadOnc-GPT, a large language model specialized for
radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on
a large dataset of radiation oncology patient records and clinical notes from
the Mayo Clinic. The model employs instruction tuning on three key tasks -
generating radiotherapy treatment regimens, determining optimal radiation
modalities, and providing diagnostic descriptions/ICD codes based on patient
diagnostic details. Evaluations conducted by having radiation oncologists
compare RadOnc-GPT impressions to general large language model impressions
showed that RadOnc-GPT generated outputs with significantly improved clarity,
specificity, and clinical relevance. The study demonstrated the potential of
using large language models fine-tuned using domain-specific knowledge like
RadOnc-GPT to achieve transformational capabilities in highly specialized
healthcare fields such as radiation oncology.

本文提出了 RadOnc-GPT，这是一个专为放射肿瘤治疗而设计的大型语言模型，通过先进的调优方法进行专项调优。RadOnc-GPT 在 Mayo Clinic 的放射肿瘤学患者记录和临床笔记的大型数据集上进行了微调。该模型在三个关键任务上进行了指令调优，包括生成放射治疗方案、确定最佳放射疗法，以及根据患者诊断详情提供诊断描述 / ICD 代码。将 RadOnc-GPT 生成的输出与通用大型语言模型的输出进行比较的放射肿瘤学家评估显示，RadOnc-GPT 的输出在清晰度、特异性和临床相关性方面有显著改善。本研究证明了使用像 RadOnc-GPT 这样通过领域特定知识进行调优的大型语言模型，在放射肿瘤学等高度专业的医疗领域实现转型能力的潜力。