Evidence map›Paper›PMID 40022253›Full record

ArticleResearch integrity and peer review2025

Policies on artificial intelligence chatbots among academic publishers: a cross-sectional audit.

Daivat Bhavsar, Laura Duffy, Hamin Jo, Cynthia Lokker, R Brian Haynes, Alfonso Iorio, Ana Marusic, Jeremy Y Ng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research integrity and peer review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
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  4. Review
  5. Article
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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

8 authors.

Daivat BhavsarDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0003-3682-918X
Laura DuffyDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0009-0007-3297-7747
Hamin JoDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0009-0000-5863-2463
Cynthia LokkerDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0003-2436-4290
R Brian HaynesDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0002-1453-3196
Alfonso IorioDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID http://orcid.org/0000-0002-3331-8766
Ana MarusicDepartment of Research in Biomedicine and Health and Center for Evidence-Based Medicine, School of Medicine, University of Split, Split, Croatia.ORCID http://orcid.org/0000-0001-6272-0917
Jeremy Y NgDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada. ngjy2@mcmaster.ca.ORCID http://orcid.org/0000-0003-0031-5873

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) chatbots are novel computer programs that can generate text or content in a natural language format. Academic publishers are adapting to the transformative role of AI chatbots in producing or facilitating scientific research. This study aimed to examine the policies established by scientific, technical, and medical academic publishers for defining and regulating the authors' responsible use of AI chatbots.

methodsThis study performed a cross-sectional audit on the publicly available policies of 162 academic publishers, indexed as members of the International Association of the Scientific, Technical, and Medical Publishers (STM). Data extraction of publicly available policies on the webpages of all STM academic publishers was performed independently, in duplicate, with content analysis reviewed by a third contributor (September 2023-December 2023). Data was categorized into policy elements, such as 'proofreading' and 'image generation'. Counts and percentages of 'yes' (i.e., permitted), 'no', and 'no available information' (NAI) were established for each policy element.

resultsA total of 56/162 (34.6%) STM academic publishers had a publicly available policy guiding the authors' use of AI chatbots. No policy allowed authorship for AI chatbots (or other AI tool). Most (49/56 or 87.5%) required specific disclosure of AI chatbot use. Four policies/publishers placed a complete ban on the use of AI chatbots by authors.

conclusionsOnly a third of STM academic publishers had publicly available policies as of December 2023. A re-examination of all STM members in 12-18 months may uncover evolving approaches toward AI chatbot use with more academic publishers having a policy.

Indexed as

Academic publishersAI chatbotsArtificial intelligenceAuthor guidelinesChatGPTPolicies

Identifiers

PMID40022253
PMCPMC11869395

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.