Application and Challenges of Large Language Models in Clinical Nursing
Year: 2025
Type: article
Study design: Systematic Review
Abstract: This review aims to summarize the current status, future prospects, and challenges of large language models in the field of clinical nursing. This systematic review follows the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for literature selection and analysis. By searching databases such as EMBASE, CINAHL, MEDLINE, and Web of Science, with a time frame from January 1, 2021, to October 23, 2024, 15 eligible studies were included. The analysis results indicate that large language models can significantly improve the efficiency and quality of nursing services, including clinical decision support, patient education, nursing documentation generation, and workflow optimization. However, several challenges were also identified in practical applications, such as data privacy protection, misleading model outputs, and ethical issues. Despite these challenges, large language models hold great potential in the nursing field. The paper discusses several solutions to address these challenges and looks ahead to the future development directions of large language model technology, with the expectation that it will be more widely and deeply applied in the clinical nursing field.
Authors: Jiaojiao Song, Wenlong Liu, Yazhe Wang, Xin Hu, Lina Chen, Xin Wu, Congru Zheng, Qing Gu
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411373370