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 in Arizona. 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 comparing RadOnc-GPT outputs to general large language model outputs 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.

翻译：本文提出RadOnc-GPT，一种通过先进微调方法专用于放射肿瘤学领域的大型语言模型。RadOnc-GPT基于亚利桑那州梅奥诊所的大量放射肿瘤学患者记录和临床笔记数据集进行微调。该模型在三个关键任务上采用指令微调——生成放射治疗方案、确定最佳放射方式，以及根据患者诊断详情提供诊断描述/ICD编码。通过将RadOnc-GPT的输出与通用大型语言模型的输出进行对比评估，结果显示RadOnc-GPT生成的输出在清晰度、特异性和临床相关性方面均有显著提升。本研究证明了利用领域特定知识微调的大型语言模型（如RadOnc-GPT）在放射肿瘤学等高度专业化医疗领域实现变革性能力的潜力。