  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.
