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 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 higher ROUGE scores in these three tasks. 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. However, our model's clinical relevance requires confirmation, and it specializes in only the aforementioned three specific tasks and lacks broader applicability. Furthermore, its evaluation through ROUGE scores might not reflect the true semantic and clinical accuracy - challenges we intend to address in future research.

翻译：本文介绍了通过先进微调方法专门针对放射肿瘤学领域的大语言模型RadOnc-GPT。该模型基于亚利桑那州梅奥诊所的放射肿瘤学患者记录大数据集进行微调。模型采用指令微调技术处理三项关键任务：生成放疗治疗方案、确定最优放射治疗模式、以及根据患者诊断详情提供诊断描述/ICD编码。通过将RadOnc-GPT输出与通用大语言模型输出进行对比评估，发现在这三项任务中其ROUGE得分更高。研究表明，利用RadOnc-GPT这类基于领域特定知识微调的大语言模型，可在放射肿瘤学等高度专业化的医疗领域实现变革性能力。然而，该模型的临床相关性仍需验证，且仅专注于上述三项特定任务，缺乏更广泛的适用性。此外，通过ROUGE得分进行的评估可能无法反映真实的语义和临床准确性——我们计划在未来的研究中解决这些挑战。