Abstract：
BACKGROUND: The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession.
METHODS: A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis.
RESULTS: The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations.
CONCLUSIONS: This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism.
CONCLUSIONS: Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.
METHODS: A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis.
RESULTS: The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations.
CONCLUSIONS: This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism.
CONCLUSIONS: Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.
摘要：
背景：近年来，大型语言模型在商业和消费者环境中的应用呈指数级增长。然而,在大型语言模型如何支持护理实践方面，文献中存在差距，教育,和研究。本研究旨在综合有关护理专业中大型语言模型的当前和潜在用途的现有文献。
方法：快速回顾文献，在Cochrane快速审查方法和PRISMA报告标准的指导下，进行了。一位专家的健康馆员协助制定了广泛的纳入标准，以说明与大型语言模型有关的文献的新兴性质。三个电子数据库(即，PubMed,CINAHL,和Embase）在2023年8月进行了搜索，以确定相关文献。讨论发展的文章，使用,并纳入护理中大型语言模型的应用进行分析。
结果：文献检索确定了总共2028篇符合纳入标准的文章。在系统地审阅摘要后，titles,和全文，最终分析包括30篇文章。几乎所有(93%；n=28)的文章都使用ChatGPT作为例子，随后讨论了大型语言模式在护理教育中的使用和价值(47%；n=14)，临床实践（40%；n=12），和研究（10%；n=3）。虽然大型语言模型的最常见评估是通过人类评估进行的（26.7%；n=8），这项分析还确定了护理中大型语言模型的常见局限性，包括缺乏系统的评估，以及其他道德和法律考虑。
结论：这是第一篇综述，旨在总结当代有关大型语言模型在护理实践中的当前和潜在用途的文献。教育,和研究。尽管存在应用大型语言模型的重要机会，在护理中使用和采用这些模式引发了一系列挑战，比如与偏见相关的伦理问题，误用，和抄袭。
结论：鉴于大型语言模型的相对新颖性，正在努力制定和实施有意义的评估，评估，标准,并建议在护理中应用大型语言模型的指南，以确保适当的，准确,和安全使用。需要未来的研究以及临床和教育合作伙伴关系，以增强对护理和医疗保健中大型语言模型的理解和应用。
方法：快速回顾文献，在Cochrane快速审查方法和PRISMA报告标准的指导下，进行了。一位专家的健康馆员协助制定了广泛的纳入标准，以说明与大型语言模型有关的文献的新兴性质。三个电子数据库(即，PubMed,CINAHL,和Embase）在2023年8月进行了搜索，以确定相关文献。讨论发展的文章，使用,并纳入护理中大型语言模型的应用进行分析。
结果：文献检索确定了总共2028篇符合纳入标准的文章。在系统地审阅摘要后，titles,和全文，最终分析包括30篇文章。几乎所有(93%；n=28)的文章都使用ChatGPT作为例子，随后讨论了大型语言模式在护理教育中的使用和价值(47%；n=14)，临床实践（40%；n=12），和研究（10%；n=3）。虽然大型语言模型的最常见评估是通过人类评估进行的（26.7%；n=8），这项分析还确定了护理中大型语言模型的常见局限性，包括缺乏系统的评估，以及其他道德和法律考虑。
结论：这是第一篇综述，旨在总结当代有关大型语言模型在护理实践中的当前和潜在用途的文献。教育,和研究。尽管存在应用大型语言模型的重要机会，在护理中使用和采用这些模式引发了一系列挑战，比如与偏见相关的伦理问题，误用，和抄袭。
结论：鉴于大型语言模型的相对新颖性，正在努力制定和实施有意义的评估，评估，标准,并建议在护理中应用大型语言模型的指南，以确保适当的，准确,和安全使用。需要未来的研究以及临床和教育合作伙伴关系，以增强对护理和医疗保健中大型语言模型的理解和应用。
