Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnostic and prognostic models. NLP enables the conversion of unstructured clinical text into structured data that can be fed into AI algorithms. The emergence of the transformer architecture and large language models (LLMs) has led to remarkable advances in NLP for various healthcare tasks, such as entity recognition, relation extraction, sentence similarity, text summarization, and question answering. In this article, we review the major technical innovations that underpin modern NLP models and present state-of-the-art NLP applications that employ LLMs in radiation oncology research. However, these LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation before clinical deployment. As such, we propose a comprehensive framework for assessing the NLP models based on their purpose and clinical fit, technical performance, bias and trust, legal and ethical implications, and quality assurance, prior to implementation in clinical radiation oncology. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.

翻译：自然语言处理（NLP）是开发利用电子健康记录（EHR）数据构建诊断和预后模型的医学人工智能（AI）系统的关键技术。NLP能够将非结构化临床文本转换为可输入AI算法的结构化数据。Transformer架构和大语言模型（LLMs）的出现推动了NLP在医疗领域多项任务中的显著进展，包括实体识别、关系抽取、句子相似度计算、文本摘要和问答系统。本文综述了支撑现代NLP模型的主要技术创新，并介绍了在放射肿瘤学研究中采用LLMs的先进NLP应用。然而，这些LLMs容易出现幻觉、偏见和伦理违规等错误，因此在临床部署前需要进行严格评估和验证。为此，我们提出一个综合评估框架，在临床放射肿瘤学实施前，基于模型的应用目的与临床适配度、技术性能、偏差与可信度、法律与伦理影响以及质量保证等维度对NLP模型进行评估。本文旨在为有兴趣在临床放射肿瘤学领域开发与应用NLP模型的研究人员和临床医生提供指导与见解。