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

• URL: http://arxiv.org/abs/2311.02205v2
• Date: Wed, 8 Nov 2023 11:51:16 GMT
• Title: An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology
• Authors: Reza Khanmohammadi, Mohammad M. Ghassemi, Kyle Verdecchia, Ahmed I. Ghanem, Luo Bing, Indrin J. Chetty, Hassan Bagher-Ebadian, Farzan Siddiqui, Mohamed Elshaikh, Benjamin Movsas, Kundan Thind
• Abstract summary: We present state-of-the-art NLP applications that employ large language models (LLMs) in radiation oncology research. LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation. 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.
• Score: 1.2714439146420664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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