Evidence map›Paper›PMID 41128585›Full record

ArticleThe American journal of nursing2025

Using Artificial Intelligence for Scholarly Writing.

Marilyn H Oermann, Jacqueline K Owens, Heather Carter-Templeton, Gabriel Peterson, Hannah E Bailey

Abstract read
In one paragraph

Article in The American journal of nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. AI in Oncology: Opportunity and Accountability.Journal of the advanced practitioner in oncology · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Marilyn H OermannMarilyn H. Oermann is Thelma M. Ingles Professor of Nursing at the Duke University School of Nursing, Durham, NC, and editor-in-chief, Nurse Educator. Jacqueline K. Owens is professor emerita at the Ashland University Schar College of Nursing and Health Sciences, Grafton, OH, and editor-in-chief, OJIN: The Online Journal of Issues in Nursing. Heather Carter-Templeton is associate professor at the West Virginia University School of Nursing, Morgantown, and editor, CIN: Computers, Informatics, Nursing. Gabriel Peterson is associate professor at the North Carolina Central University School of Library and Information Sciences, Durham. Hannah E. Bailey is a data analyst at Data Driven WV, West Virginia University, Morgantown. Contact author: Marilyn H. Oermann, marilyn.oermann@duke.edu. The authors have disclosed no potential conflicts of interest, financial or otherwise.
Jacqueline K Owens
Heather Carter-Templeton
Gabriel Peterson
Hannah E Bailey

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

abstractThe widespread availability of generative artificial intelligence (genAI) continues to transform the scholarly communication process. With wide access to genAI tools, authors now not only have the benefits these tools can provide, such as creation of text, tables, and figures, but also the responsibility to use these tools with integrity and transparency. Examples of concerns about the use of genAI tools include ethical and legal breaches; inaccurate, biased, or fabricated content; and lack of accountability. Given the potential for serious harm to patients as well as the undermining of the credibility of scholarly communication with the use of unchecked content, it is essential for nurse authors to also include their judgment and subject matter expertise in the preparation of a scholarly manuscript that includes AI-generated information. This article offers a brief overview of recent research findings related to the use of genAI tools to support scholarly writing and provides guidelines for clinicians, educators, and other nurse authors on the appropriate use of AI in the preparation of manuscripts. Information is also provided about authorship, accuracy of content and references, biases and misrepresentations within AI-generated content, plagiarism, and appropriate disclosure of AI tools in manuscript preparation.

Indexed as

Artificial IntelligencePublishingScholarly CommunicationWritingAuthorshipHumansAI biaschatbotsgenerative artificial intelligencescholarly communicationwriting for publication

Identifiers

PMID41128585
PMCPMC12548816

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.