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. 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. 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. 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. 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.

中文翻译：

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快速回顾大语言模型在护理中的当前和潜在用途

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，关于大型语言模型如何支持护理实践、教育和研究的文献中存在空白。本研究旨在综合有关护理行业大型语言模型当前和潜在用途的现有文献。在 Cochrane 快速审查方法和 PRISMA 报告标准的指导下，对文献进行了快速审查。一位健康图书馆专家协助制定了广泛的纳入标准，以考虑与大语言模型相关的文献的新兴性质。检索了三个电子数据库（即 PubMed、CINAHL 和 Embase）以识别 2023 年 8 月的相关文献。讨论护理领域大语言模型的开发、使用和应用的文章被纳入分析。文献检索共找到 2028 篇符合纳入标准的文章。经过系统审查摘要、标题和全文，最终分析纳入 30 篇文章。几乎所有纳入的文章（93%；n = 28）都以 ChatGPT 为例，随后讨论了大语言模型在护理教育（47%；n = 14）、临床实践（40%；n）中的使用和价值。 = 12) 和研究 (10 %; n = 3)。虽然大语言模型最常见的评估是通过人类评估进行的（26.7%；n = 8），但该分析还确定了护理中大语言模型的常见局限性，包括缺乏系统评估以及其他伦理和法律考虑。这是第一篇总结当代文献的综述，涉及大语言模型在护理实践、教育和研究中的当前和潜在用途。尽管应用大型语言模型有很大的机会，但这些模型在护理领域的使用和采用引发了一系列挑战，例如与偏见、滥用和剽窃相关的伦理问题。鉴于大语言模型相对新颖，建议持续努力制定和实施有意义的评估、评价、标准和指南，以确保在护理中应用大语言模型，以确保适当、准确和安全的使用。未来的研究需要与临床和教育合作伙伴一起加强对大语言模型在护理和医疗保健领域的理解和应用。