Evidence map›Paper›PMID 40916039›Full record

ArticleResearch integrity and peer review2025

Attitudes and perceptions of biomedical journal editors in chief towards the use of artificial intelligence chatbots in the scholarly publishing process: a cross-sectional survey.

Jeremy Y Ng, Malvika Krishnamurthy, Gursimran Deol, Wid Al-Zahraa Al-Khafaji, Vetrivel Balaji, Magdalene Abebe, Jyot Adhvaryu, Tejas Karrthik, Pranavee Mohanakanthan, Adharva Vellaparambil and 7 more

Abstract read
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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 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. Review
  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

17 authors.

Jeremy Y NgCentre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, ON, Canada. ngjy2@mcmaster.ca.ORCID http://orcid.org/0000-0003-0031-5873
Malvika KrishnamurthyInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0009-8548-1029
Gursimran DeolInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0000-1555-6384
Wid Al-Zahraa Al-KhafajiInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0005-0900-6200
Vetrivel BalajiDepartment of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Canada.ORCID http://orcid.org/0009-0003-0860-6795
Magdalene AbebeInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0005-4717-4213
Jyot AdhvaryuInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0000-3979-1610
Tejas KarrthikInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0009-3065-392X
Pranavee MohanakanthanInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0006-3087-2960
Adharva VellaparambilInstitute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.ORCID http://orcid.org/0009-0000-2188-5715
Lex M BouterDepartment of Epidemiology and Data Sciences, Amsterdam Universities Medical Center, Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-2659-5482
R Brian HaynesDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, 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, ON, Canada.ORCID http://orcid.org/0000-0002-3331-8766
Cynthia LokkerDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.ORCID http://orcid.org/0000-0003-2436-4290
Hervé Maisonneuve, Consultant, Lyon, France.ORCID http://orcid.org/0000-0001-8365-7558
Ana MarušićDepartment 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
David MoherCentre for Journalology, Ottawa Methods Centre, Ottawa Hospital Research Institute, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0003-2434-4206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence chatbots (AICs) are designed to mimic human conversations through text or speech, offering both opportunities and challenges in scholarly publishing. While journal policies of AICs are becoming more defined, there is still a limited understanding of how Editors in chief (EiCs) of biomedical journals' view these tools. This survey examined EiCs' attitudes and perceptions, highlighting positive aspects, such as language and grammar support, and concerns regarding setup time, training requirements, and ethical considerations towards the use of AICs in the scholarly publishing process.

methodsA cross-sectional survey was conducted, targeting EiCs of biomedical journals across multiple publishers. Of 3725 journals screened, 3381 eligible emails were identified through web scraping and manual verification. Survey invitations were sent to all identified EiCs. The survey remained open for five weeks, with three follow-up email reminders.

resultsThe survey had a response rate of 16.5% (510 total responses) and a completion rate of 87.0%. Most respondents were familiar with AIs (66.7%), however, most had not utilized AICs in their editorial work (83.7%) and many expressed interest in further training (64.4%). EiCs acknowledged benefits such as language and grammar support (70.8%) but expressed mixed attitudes on AIC roles in accelerating peer review. Perceptions included the initial time and resources required for setup (83.7%), training needs (83.9%), and ethical considerations (80.6%).

conclusionsThis study found that EiCs have mixed attitudes toward AICs, with some EICs acknowledging their potential to enhance editorial efficiency, particularly in tasks like language editing, while others expressed concerns about the ethical implications, the time and resources required for implementation, and the need for additional training.

Indexed as

Artificial intelligenceArtificial intelligence chatbotsEditor in chiefMedical journalsScholarly publishing

Identifiers

PMID40916039
PMCPMC12416066

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.