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Natural Language Processing (NLP) is a key technique for developing MedicalArtificial Intelligence (AI) systems that leverage Electronic Health Record(EHR) data to build diagnostic and prognostic models. NLP enables theconversion of unstructured clinical text into structured data that can be fedinto AI algorithms. The emergence of the transformer architecture and largelanguage models (LLMs) has led to remarkable advances in NLP for varioushealthcare tasks, such as entity recognition, relation extraction, sentencesimilarity, text summarization, and question answering. In this article, wereview the major technical innovations that underpin modern NLP models andpresent state-of-the-art NLP applications that employ LLMs in radiationoncology research. However, these LLMs are prone to many errors such ashallucinations, biases, and ethical violations, which necessitate rigorousevaluation and validation before clinical deployment. As such, we propose acomprehensive framework for assessing the NLP models based on their purpose andclinical fit, technical performance, bias and trust, legal and ethicalimplications, and quality assurance, prior to implementation in clinicalradiation oncology. Our article aims to provide guidance and insights forresearchers and clinicians who are interested in developing and using NLPmodels in clinical radiation oncology.

An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology