JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v8i6.9015ArticleThe Auxiliary Role of Large Language Models in Clinical Dialogueshttps://artdesignp.com/journal/JERA/8/6/10.26689/jera.v8i6.9015LuoHaipeng,ZhangYuchu2024862024-12-02In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP). These models not only perform well in a variety of language tasks but also show great potential in the medical field. This paper aims to explore the application of LLMs in clinical dialogues, analyzing their role in improving the efficiency of doctor-patient communication, aiding in diagnosis and treatment, and providing emotional support. The paper also discusses the challenges and limitations of the model in terms of privacy protection, ethical issues, and practical applications. Through comprehensive analysis, we conclude that applying LLMs in clinical dialogues is promising. However, it requires careful consideration and caution by practitioners in practice.

Kang Y, Guo Q, Zhang W, et al., 2023, Knowledge-enhanced Medical Language Models: Current Status, Techniques, and Applications. Journal of Medical Informatics, 2023(9): 12–22.

Kaul V, Enslin S, Gross SA, 2020, History of Artificial Intelligence in Medicine. Gastrointest Endosc, 92(4): 807–812.

Ma W, Gong M, Dai H, et al., 2023, A Comprehensive Review of the Applications of Large Language Models in Clinical Medicine with ChatGPT as a Representative. Journal of Medical Informatics, 2023(7): 9–17.

Radford A, Narasimhan K, Salimans T, et al., 2018, Improving Language Understanding by Generative Pre-Training. Published June 11, 2018. https://openai.com/blog/language-unsupervised/

Nelson BD, Alshehri AS, Ajagekar A, et al., 2024, Generative AI and Process Systems Engineering: The Next Frontier. Computers and Chemical Engineering, 187: 108723.

Jiang G, Ma Z, Zhang L, et al., 2024, EPlus-LLM: A Large Language Model-Based Computing Platform for Automated Building Energy Modeling. Applied Energy, 367: 123431.

Yang L, Wang Z, Li Z, et al., 2024, An Empirical Study of Multimodal Entity-Based Sentiment Analysis with ChatGPT: Improving In-Context Learning via Entity-Aware Contrastive Learning. Information Processing and Management, 61(4): 103724.

Luo H, 2024, Demand Analysis and Talents Training of International Medical Language Services in the Context of Digitalization. Modern Management Forum, 2024(8): 162–164.

Feng Z, Zhang D, 2024, Large Language Model in Artificial Intelligence. Foreign Languages and Literature, 2024(5): 1–29.

Sudharson D, Noor SSM, Oviya RS, et al., 2024, A Data Driven AI Framework for Conversational Bot by Vision Transformers in Health Care Systems, 2024: 48–60.

Yang L, Wang Z, Li Z, et al., 2024, An Empirical Study of Multimodal Entity-Based Sentiment Analysis with ChatGPT: Improving In-Context Learning via Entity-Aware Contrastive Learning. Information Processing and Management, 61(4): 103724.

Hagendorff T, 2024, Deception Abilities Emerged in Large Language Models. Proceedings of the National Academy of Sciences of the United States of America, 121(24): e2317967121–e2317967121.

Huang M, Zhao J, Yu T, 2020, A Review on Research and Application of Medical Large Language Models. Chinese Journal of Medical Library and Information Science, 2020(11): 39–46.

Landman R, Healey SP, Loprinzo V, et al., 2024, Using Large Language Models for Safety-Related Table Summarization in Clinical Study Reports. JAMIA Open, 7(2): ooae043.

Ruan T, Bian Y, Yu G, et al., 2023, Medical Big Data-Based Pre-Trained Language Model and Classification of its Medical Texts. Chinese Journal of Health Informatics and Management, 2023(11): 853–861.