We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has received limited attention from the NLP community in the past. With the advent of Artificial General Intelligence (AGI), there is an increasing need for specialized datasets and benchmarks to facilitate research and development. ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration. It encompasses various NLP tasks including Logic Reasoning, Text Classification, Named Entity Recognition (NER), Question Answering (QA), Text Summarization, and Patient-Clinician Conversations, each with a distinct focus on radiation oncology concepts and application cases. In addition, we have developed an instruction-tuning dataset consisting of over 20k instruction pairs (based on ROND) and trained a large language model, CancerChat. This serves to demonstrate the potential of instruction-tuning large language models within a highly-specialized medical domain. The evaluation results in this study could serve as baseline results for future research. ROND aims to stimulate advancements in radiation oncology and clinical NLP by offering a platform for testing and improving algorithms and models in a domain-specific context. The ROND dataset is a joint effort of multiple U.S. health institutions. The data is available at https://github.com/zl-liu/Radiation-Oncology-NLP-Database.

我们提供了放射治疗自然语言处理数据库（ROND），这是放射治疗领域的首个专用NLP数据集，旨在为该领域的研究和发展提供专门设计的数据集和基准。ROND涵盖了多种NLP任务，包括逻辑推理、文本分类、命名实体识别（NER）、问答（QA）、文本摘要和患者-临床医师对话，每种任务都对放射治疗概念和应用案例有着明确的关注点。此外，我们还通过基于ROND的20k指令对数据集开展了调整，并训练了一个大型语言模型CancerChat，以展示在高度专业化的医疗领域中指令调整大型语言模型的潜力。本研究的评估结果可为未来研究提供基准结果。ROND旨在为放射治疗和临床NLP的进展提供一个测试和改进算法和模型的领域特定上下文的平台。ROND数据集是多个美国卫生机构的联合工作。数据可在以下URL获得：