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
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Abstract
Background: Artificial 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. Methods: A 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. Results: The 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%). Conclusions: This 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.
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The same surveys reveal widespread uncertainty about how to begin, with many researchers reporting limited familiarity with existing tools, limited confidence in their ability to use them responsibly, and limited institutional guidance on best practices.
“Second, the same surveys reveal widespread uncertainty about how to begin.Many researchers report limited familiarity with existing tools, limited confidence in their own ability to use them responsibly, and limited institutional guidance on best practices.”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [6]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredDifferent surveys report mixed, but generally increasing adoption of AI by the scientific community.
“Different surveys report mixed, but generally increasing adoption of AI by the scientific community.”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [6]
The Introduction (page not provided) says AI chatbots are increasingly used in academic contexts, and the Discussion (page not provided) reports mixed views among biomedical journal editors. But this cross-sectional survey found limited use in editors’ work; it does not show that different surveys establish a generally increasing adoption trend across the scientific community. (LLM, conf=0.82)
Experimental — accuracy not yet measuredInterest in using AI to support research is extremely high.
“First, interest in using AI to support research is extremely high (Fig. 1).”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [6]
The Discussion (page unavailable) reports notable interest in using AI chatbots for research support, and the Results say 77% of surveyed biomedical editors in chief considered them important to future scientific research. Neither passage establishes that interest in using AI to support research is “extremely high” generally; the Discussion also notes concerns about accuracy and transparency. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSurveys across the scientific community consistently show three dominant themes: high interest, uncertainty about how to begin, and concerns about accuracy, bias, reproducibility, and erosion of critical thinking.
“Surveys across the scientific community consistently show three dominant themes [4][5][6][7][8][9][10][11][12] .”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [6]
The Discussion (no page number in our copy) reports editors’ interest in AI chatbots, limited training, and concerns about bias and reliability; the Introduction also reports researchers’ concerns about accuracy and reproducibility. But this survey is limited to biomedical journal editors in chief, and our copy does not establish three consistent themes across the scientific community or identify erosion of critical thinking as one of them. (LLM, conf=0.84)
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Cites
A General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858 · see citing works
Elm, E. V., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007) · BMJ · doi:10.1136/bmj.39335.541782.ad · Cited by 10,766 · see citing works
Eysenbach, G. (2004) · Journal of Medical Internet Research · doi:10.2196/jmir.6.3.e34 · Cited by 6,480 · cited 1× · see citing works
“Strength-ening the Reporting of Observational Studies in Epidemiology (STROBE) and Checklist for Reporting Results of Internet E-Surveys (CHERRIES) guidelines were used to inform the reporting of this survey study [36, 37].”
Cost-Throughput Analysis of a Modified TCP for Optical Burst Switched Networks
Datta, S., Dutta, A., & Choudhury, S. (2012) · International Journal of Computer Applications · doi:10.5120/9773-4312 · Cited by 4,370 · cited 1× · see citing works
“The survey was administered online through the University of Ottawa’s version of the Sur-veyMonkey software [34].”
An Overview of Chatbot Technology
Adamopoulou, E., & Moussiades, L. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-49186-4_31 · Cited by 1,129 · see citing works
F.Marks, D., & Yardley, L. (2004) · SAGE Publications Ltd eBooks · doi:10.4135/9781849209793.n4 · Cited by 1,123 · cited 2× · see citing works
“The initial codes were then grouped into overarching themes, based on patterns, similarities, and common-alities observed in the data [38, 39].”
Beins, B. C. (2013) · The Encyclopedia of Cross‐Cultural Psychology · doi:10.1002/9781118339893.wbeccp501 · Cited by 864 · see citing works
Yates, M. (2009) · SIGGRAPH ASIA Animations · doi:10.1145/1665208.1665246 · Cited by 723
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08215-8 · Cited by 434 · see citing works
Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/12887 · Cited by 384 · cited 1× · see citing works
“Among various AI applications, artificial intelligence chatbots (AICs) represent a rapidly advancing category of tools that use large language models (LLMs) and deep learning algorithms to simulate human-like dialogue [4–6].”
AI-generated research paper fabrication and plagiarism in the scientific community
Elali, F. R., & Rachid, L. N. (2023) · Patterns · doi:10.1016/j.patter.2023.100706 · Cited by 197 · cited 1× · see citing works
“Addressing these challenges proactively will ensure that AICs serve as a valuable complement to, rather than a replacement for, human editorial expertise.”
Artificial intelligence to support publishing and peer review: A summary and review
Kousha, K., & Thelwall, M. (2023) · Learned Publishing · doi:10.1002/leap.1570 · Cited by 162 · cited 1× · see citing works
“[43] Our study aligns with these findings, where EiCs recognized AICs’ utility in performing routine tasks such as language checks and plagiarism screening.”
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160 · cited 1× · see citing works
“…review of 26 published reporting guidelines for medical AI research, which identified substantial variation in scope, consensus quality, and applicability across research phases, underscoring the need for harmonized standards to improve reliability, reproducibility, and public trust [48].”
ChatGPT: promise and challenges for deployment in low- and middle-income countries
Wang, X., Sanders, H. M., Liu, Y., Seang, K., Tran, B. X., Atanasov, A. G., Qiu, Y., Tang, S., Car, J., Wang, Y. X., et al. (2023) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2023.100905 · Cited by 143 · cited 2× · see citing works
“We must also acknowledge non-response bias and selective response bias, which may affect the internal validity and generalizability of the collected data [51].”
Scalable watermarking for identifying large language model outputs
Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., et al. (2024) · Nature · doi:10.1038/s41586-024-08025-4 · Cited by 132 · cited 1× · see citing works
“Addressing these challenges proactively will ensure that AICs serve as a valuable complement to, rather than a replacement for, human editorial expertise.”
Loh, E. (2024) · BMJ Leader · doi:10.1136/leader-2023-000797 · Cited by 116 · see citing works
Zielinski, C., Winker, M. A., Aggarwal, R., Ferris, L. E., Heinemann, M., Lapeña, J. F., Pai, S. A., Ing, E., & Citrome, L. (2023) · National Medical Journal of India · doi:10.21608/aeji.2023.282936 · Cited by 97 · see citing works
Use of artificial intelligence and the future of peer review
Bauchner, H., & Rivara, F. (2024) · Health affairs scholar · doi:10.1093/haschl/qxae058 · Cited by 77 · cited 1× · see citing works
“AI applications have been tested for a range of tasks including triaging submissions, assessing manuscript completeness, detecting ethical issues such as plagiarism or inappropriate authorship, and even assisting in reviewer selection [14].”
Woods, W. A., Watson, M., Ranaweera, S., Tajuria, G., & Sumathipala, A. (2023) · Global Public Health · doi:10.1080/17441692.2023.2229890 · Cited by 76 · see citing works
Ng, J. Y., Maduranayagam, S. G., Suthakar, N., Li, A., Lokker, C., Iorio, A., Haynes, R. B., & Moher, D. (2025) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00202-4 · Cited by 64 · cited 5× · see citing works
“While earlier studies have focused on researcher attitudes and publisher policies, few have directly investigated how EiCs perceive the integration of AICs into peer review and editorial decision-making [11].”
InfluentialSelf-citationPolicies on artificial intelligence chatbots among academic publishers: a cross-sectional audit
Bhavsar, D., Duffy, L., Jo, H., Lokker, C., Haynes, R. B., Iorio, A., Marušić, A., & Ng, J. Y. (2025) · Research Integrity and Peer Review · doi:10.1186/s41073-025-00158-y · Cited by 39 · see citing works
Self-citationAccelerating Editorial Processes in Scientific Journals: Leveraging AI for Rapid Manuscript Review
Fiorillo, L., & Mehta, V. (2024) · Oral Oncology Reports · doi:10.1016/j.oor.2024.100511 · Cited by 38 · cited 1× · see citing works
“Fiorillo & Mehta explored AICs’ potential to reduce editorial bias but also noted risks like algorithmic bias and data manipulation [21].”
Memon, A., Ahmed, I., Ghaffar, N., Ahmed, K., & Sadiq, I. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2021-105042 · Cited by 32 · see citing works
Yin, S., Huang, S., Xue, P., Xu, Z., Lian, Z., Ye, C., Ma, S., Liu, M., Hu, Y., Lu, P., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-03899-1 · Cited by 21 · see citing works
How artificial intelligence will affect the future of medical publishing
Vincent, J.-L. (2023) · Critical Care · doi:10.1186/s13054-023-04511-9 · Cited by 11 · cited 1× · see citing works
“To address these issues, the International Association of Scientific, Technical & Medical Publishers (STM) released guidelines emphasizing transparency, intellectual property protection, confidentiality, and ethical oversight in the use of AI [18, 19].”
The advent of human-assisted peer review by AI
(2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01228-0 · Cited by 8 · cited 1× · see citing works
“The introduction of AICs into these workflows presents opportunities to reduce manual burden and improve decision-making speed and consistency [25].”
Artificial Intelligence-Assisted Editorial Tools: Case Studies
Lazarus, E. (2021) · Science Editing · doi:10.36591/se-d-4404-e7 · Cited by 4 · cited 1×
“Recent studies also suggest that AI could support editorial workflows by helping to identify submission quality, flagging methodological inconsistencies, and highlighting areas needing editorial attention [20–22].”
Turner, J. R. (2023) · Performance Improvement Quarterly · doi:10.56811/piq-24-0004 · Cited by 3 · cited 1×
“…frameworks, including those from Committee on Publication Ethics (COPE), the World Association of Medical Editors (WAME), and the European Commission emphasize the importance of transparency in the use of AICs and recommend disclosure to authors without explicitly requiring written consent [45–47].”
Barić, H., Škorić, L., & Kalanj-Bognar, S. (2024) · Croatian Medical Journal · doi:10.3325/cmj.2024.65.471 · Cited by 2
Flagged first, then by citations
29 of the 58 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- [23]Editorial criteria and processes. Nature. Available from: https://www.nature.com/nature/for-authors/editorial-criteria-and-processes. Cited 3 Apr 2025.
- [26]Ng JY, Krishnamurthy M, Balaji V, Bouter L, Haynes RB, Iorio A, et al. 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 across multiple publishers. OSF; 2024. Available from: https://osf.io/xt6f2. PMC12416066 40916039
- [27]Ng JY, Krishnamurthy M, Balaji V, Bouter L, Haynes RB, Iorio A, et al. 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 across multiple publishers. OSF; 2024. Available from: https://osf.io/d7nv5/. PMC12416066 40916039
- [40]Anderson R. Thematic content analysis (TCA). Descriptive presentation of qualitative data. 2007;3:1–4. https://rosemarieanderson.com/wp-content/uploads/2014/08/ThematicContentAnalysis.pdf.
- [44]Ng JY, Shah A, Roni E, Lokker C, Iorio A, Haynes RB, et al. Attitudes and perceptions of university students and postdoctoral fellows in biomedicine towards the use of artificial intelligence chatbots in the educational process: a large-scale, international cross-sectional survey. medRxiv. 2025:2025–06. 10.1101/2025.06.25.25328523. · DOI
Cited by
Sorted by citations
Ng, J. Y. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1745928 · Cited by 5 · see citing works
“Some journal editors and peer reviewers are also beginning to use GenAI chatbots for initial screening of manuscripts to assess elements such as structure or adherence to reporting standards (Ng et al., 2025a).”
Self-citationThe Epistemic Shift in Science Communication: Strategic Foresight for the Future of Peer Review
Biele, C., & Kowalski, J. (2026) · Science Communication · doi:10.1177/10755470261426817 · Cited by 2 · see citing works
Ng, J. Y., Lee, M. S., Gupta, M. M., Tan, J., Adapa, K., Gupta, P. K., Li, S., Mehta, D. H., Ring, M. R., Shridhar, M., et al. (2025) · International Journal of Ayurveda Research · doi:10.4103/ijar.ijar_293_25 · Cited by 2 · see citing works
Self-citationCwik, J. C. (2026) · Research Integrity and Peer Review · doi:10.1186/s41073-026-00245-8 · Cited by 2 · see citing works
“Survey evidence from biomedical editors-in-chief reinforces this need: many editors report limited direct use of AI chatbots in editing, policy uncertainty, training needs, resource constraints, and ethical concerns related to bias, transparency, accountability, and human judgment [15].”
Luckett, B., Acosta, G., Bixler, E., & Bobier, C. A. (2026) · Clinical Anatomy · doi:10.1002/ca.70151 · Cited by 1 · see citing works
Ng, J. Y. (2026) · Journal of Complementary and Integrative Medicine · doi:10.1515/jcim-2026-0082 · Cited by 1 · see citing works
Self-citationLupon, E., Bérard, Q., Migaud, H., Clavert, P., & Micicoi, G. (2026) · Orthopaedics & Traumatology: Surgery & Research · doi:10.1016/j.otsr.2026.104801
Reimagining biomedical science workflows in the age of large language models
Roberts, K. F., Koutarapu, S., Melendez, J., Landsness, E. C., Reese, J. T., Elbert, D. L., & Bateman, R. J. (2026) · npj Dementia · doi:10.1038/s44400-026-00126-3
Sebo, P., & Wang, T. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10806-8
Lupon, E. L., Bérard, Q., Migаud, H., Clavert, P., & Micicoi, G. (2026) · Revue de Chirurgie Orthopédique et Traumatologique · doi:10.1016/j.rcot.2026.07.022
Ng, J. Y., Bhavsar, D., Krishnamurthy, M., Dhanvanthry, N., Fry, D., Kim, J. W., King, A., Lai, J., Makwanda, A., Olugbemiro, P., et al. (2026) · Integrative Medicine Research · doi:10.1016/j.imr.2026.101403
Self-citationTan, C.-Y., Adams, D., Ng, D., & Gao, L. (2026) · Educational Research and Evaluation · doi:10.1080/13803611.2026.2698571
“Other reviews focus on examining specific AI tools such as chatbots (Ng et al., 2025), ChatGPT (Acut et al., 2025), Claude (Eisele-Metzger et al., 2025), Consensus (Faix, 2025), Elicit (Bianchi et al., 2025), Gemini (Do, 2025), and Llama (López-Pineda et al., 2025).”
Ng, J., Bhavsar, D., Dhanvanthry, N., Bouter, L., Chan, T. M., Cramer, H., Flanagin, A., Iorio, A., Lokker, C., Maisonneuve, H., et al. (2026) · Research Integrity and Peer Review · doi:10.1186/s41073-026-00256-5
“Overall, the study reported mixed attitudes as many EiCs would acknowledge benefits of AICs, such as enhancing proof-reading abilities, while raising concerns about initial resources required for training, and education and ethical implications of data privacy [38].”
Self-citationGenerative AI in Scientific Publishing – Opportunities and Challenges
Bradić-Martinović, A. (2025) · Conference proceedings · doi:10.31410/ai.smart.2025.135
“Automated systems now support submission triage, topic classification, and plagiarism screening, allowing editors to allocate human expertise to more substantive tasks (Ng et al., 2025).”
- Authors
- Ng, J. Y., Krishnamurthy, M., Deol, G., Al-Khafaji, W. A.-Z., Balaji, V., Abebe, M., Adhvaryu, J., Karrthik, T., Mohanakanthan, P., Vellaparambil, A., Bouter, L. M., Haynes, R. B., Iorio, A., Lokker, C., Maisonneuve, H., Marušić, A., & Moher, D.
- Affiliations
- University of Ottawa (CA)
- Universitätsklinikum Tübingen (DE)
- McMaster University (CA)
- University of Toronto (CA)
- Amsterdam University Medical Centers (NL)
- University of Split (HR)
- Corresponding author
- Ng, Jeremy Y.
- ORCID
- Venue
- Research Integrity and Peer Review
- Published
- 2025-09-08
- Volume
- 10
- Issue
- 1
- Pages
- 19-19
- ISSN
- 2058-8615 (ISSN-L)
- PMC
- PMC12416066
- OpenAlex
- W4414042524
- Cited by
- 14
- References
- 58
- Citations by year
- 2026: 11 · 2025: 3
- FWCI
- 52.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Research Integrity and Peer Review (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- VU Research Portal (submittedVersion, other-oa)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- AI chatbots, biomedical journals, scholarly publishing, editorial efficiency, AI ethics, AI training needs, attitudes toward AI, journal editorial policy
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