ArticleBMJ (Clinical research ed.)2024
Publishers' and journals' instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis.
Article in BMJ (Clinical research ed.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the retraction spotlight: trends, causes and consequences of withdrawn AI literature through a systematic bibliometric review.Frontiers in research metrics and analytics · 2025Pooled it
- Practical guide to structuring the Background section in clinical and epidemiological studies.Chiropractic & manual therapies · 2026Article
- Evolution of Artificial Intelligence Adoption inOrthopaedic journal of sports medicine · 2026Article
- Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows.Research integrity and peer review · 2026Article
- Provenance, Not Prohibition: A Framework for AI in Scholarly Publishing.Laryngoscope investigative otolaryngology · 2026Article
- A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT).Research integrity and peer review · 2026Review
- Six misconceptions about large language models: A minimal model and diagnostic taxonomy.PNAS nexus · 2026Article
- Empowering researchers and healthcare professionals in academic publishing: key insights and practical lessons from the British Society of Antimicrobial Chemotherapy (BSAC) workshop 2025.JAC-antimicrobial resistance · 2026Review
- Where do I submit my next paper? A practical guide for biomedical researchers.Journal of biomedical research · 2026Article
- Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach.BMC medical education · 2026Article
- AI humanizers and the crisis of information integrity: implications for scientific writing.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- When ophthalmology goes viral: AI-generated content and the viral spread of medical knowledge on social media.Eye (London, England) · 2026Article
- Large Language Models in Scientific Publishing: Policy Landscape for Authors, Reviewers, and Editors.Korean journal of radiology · 2026Article
- Artificial Intelligence in Scientific Writing: Balancing Innovation and Efficiency with Integrity: Perspectives and Position Statements of Global Andrology Forum Expert Group.The world journal of men's health · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Generative AI adoption and ethical perceptions: a comparative study of medical and non-medical researchers in Chinese universities.BMC medical ethics · 2026Article
- Artificial Intelligence in Medical Writing: Is It an Exception to Evidence-Based Medicine?JMA journal · 2026Article
- Use of artificial intelligence tools in the publishing process: expectations from publishers through author guidelines.Frontiers in research metrics and analytics · 2026Article
- Endorsement of artificial intelligence guidelines across leading endocrinology journals: a cross-sectional analysis.Frontiers in endocrinology · 2026Article
- The Presence and Nature of AI-Use Disclosure Statements in Medical Education Journals: A Bibliometric Study.Perspectives on medical education · 2026Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI).
designCross sectional, bibliometric study.
settingWebsites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023.
participantsTop 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact.
main outcome measuresThe primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals.
resultsAmong the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals.
conclusionsGuidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.