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A rapid review on current and potential uses of large language models in nursing

Hobensack Mollie; Von Gerich Hanna; Vyas Pankaj; Withall Jennifer; Peltonen Laura-Maria; Block Lorraine J.; Davies Shauna; Chan Ryan; Van Bulck Liesbet; Cho Hwayoung; Paquin Robert; Mitchell James; Topaz Maxim; Song Jiyoun

A rapid review on current and potential uses of large language models in nursing

Hobensack Mollie
Von Gerich Hanna
Vyas Pankaj
Withall Jennifer
Peltonen Laura-Maria
Block Lorraine J.
Davies Shauna
Chan Ryan
Van Bulck Liesbet
Cho Hwayoung
Paquin Robert
Mitchell James
Topaz Maxim
Song Jiyoun
Katso/Avaa
CC_Elsevier_Hobensack-etal_A-rapid-review-on_2024.pdf (201.3Kb)
Lataukset: 

Elsevier
doi:10.1016/j.ijnurstu.2024.104753
URI
https://doi.org/10.1016/j.ijnurstu.2024.104753
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025082791689
Tiivistelmä

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.

Discussion: 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.

Conclusion: 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.

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