Hannah E Bailey, Heather Carter-Templeton, Gabriel M Peterson, Marilyn H Oermann, Jacqueline K Owens
{"title":"Prevalence of Words and Phrases Associated With Large Language Model-Generated Text in the Nursing Literature.","authors":"Hannah E Bailey, Heather Carter-Templeton, Gabriel M Peterson, Marilyn H Oermann, Jacqueline K Owens","doi":"10.1097/CIN.0000000000001237","DOIUrl":null,"url":null,"abstract":"<p><p>All disciplines, including nursing, may be experiencing significant changes with the advent of free, publicly available generative artificial intelligence tools. Recent research has shown the difficulty in distinguishing artificial intelligence-generated text from content that is written by humans, thereby increasing the probability for unverified information shared in scholarly works. The purpose of this study was to determine the extent of generative artificial intelligence usage in published nursing articles. The Dimensions database was used to collect articles with at least one appearance of words and phrases associated with generative artificial intelligence. These articles were then searched for words or phrases known to be disproportionately associated with large language model-based generative artificial intelligence. Several nouns, verbs, adverbs, and phrases had remarkable increases in appearance starting in 2023, suggesting use of generative artificial intelligence. Nurses, authors, reviewers, and editors will likely encounter generative artificial intelligence in their work. Although these sophisticated and emerging tools are promising, we must continue to work toward developing ways to verify accuracy of their content, develop policies that insist on transparent use, and safeguard consumers of the evidence they generate.</p>","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cin-Computers Informatics Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/CIN.0000000000001237","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
All disciplines, including nursing, may be experiencing significant changes with the advent of free, publicly available generative artificial intelligence tools. Recent research has shown the difficulty in distinguishing artificial intelligence-generated text from content that is written by humans, thereby increasing the probability for unverified information shared in scholarly works. The purpose of this study was to determine the extent of generative artificial intelligence usage in published nursing articles. The Dimensions database was used to collect articles with at least one appearance of words and phrases associated with generative artificial intelligence. These articles were then searched for words or phrases known to be disproportionately associated with large language model-based generative artificial intelligence. Several nouns, verbs, adverbs, and phrases had remarkable increases in appearance starting in 2023, suggesting use of generative artificial intelligence. Nurses, authors, reviewers, and editors will likely encounter generative artificial intelligence in their work. Although these sophisticated and emerging tools are promising, we must continue to work toward developing ways to verify accuracy of their content, develop policies that insist on transparent use, and safeguard consumers of the evidence they generate.
期刊介绍:
For over 30 years, CIN: Computers, Informatics, Nursing has been at the interface of the science of information and the art of nursing, publishing articles on the latest developments in nursing informatics, research, education and administrative of health information technology. CIN connects you with colleagues as they share knowledge on implementation of electronic health records systems, design decision-support systems, incorporate evidence-based healthcare in practice, explore point-of-care computing in practice and education, and conceptually integrate nursing languages and standard data sets. Continuing education contact hours are available in every issue.