Evidence map›Paper›PMID 39934830›Full record

ArticleBMC medicine2025

Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals.

Shuhui Yin, Simu Huang, Peng Xue, Zhuoran Xu, Zi Lian, Chenfei Ye, Siyuan Ma, Mingxuan Liu, Yuanjia Hu, Peiyi Lu and 1 more

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Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. 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

11 authors.

Shuhui YinApplied Linguistics & Technology, Department of English, Iowa State University, Ames, IA, USA.
Simu HuangCenter for Data Science, Zhejiang University, Hangzhou, Zhejiang, China.
Peng XueInstitute of Chinese Medical Sciences, University of Macau, Zhuhai, Macao SAR, China.
Zhuoran XuGraduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Zi LianCenter for Health Equity & Urban Science Education, Teachers College, Columbia University, New York, NY, USA.
Chenfei YeInternational Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, Guangdong, China.
Siyuan MaDepartment of Communication, University of Macau, Zhuhai, Macao SAR, China.
Mingxuan LiuDepartment of Communication, University of Macau, Zhuhai, Macao SAR, China.
Yuanjia HuInstitute of Chinese Medical Sciences, University of Macau, Zhuhai, Macao SAR, China. yuanjiahu@um.edu.mo.
Peiyi LuDepartment of Social Work and Social Administration, University of Hong Kong, Hong Kong SAR, China. peiyilu@hku.hk.
Chihua LiInstitute of Chinese Medical Sciences, University of Macau, Zhuhai, Macao SAR, China. chihuali@umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of recommendations of GAI usage guidelines among medical journals and how these factors relate to journal characteristics.

methodsFrom the SCImago Journal Rank (SJR) list for medicine in 2022, we generated two groups of journals: top SJR ranked journals (N = 200) and random sample of non-top SJR ranked journals (N = 140). For each group, we examined the coverage of author and reviewer guidelines across four categories: no guidelines, external guidelines only, own guidelines only, and own and external guidelines. We then calculated the number of recommendations by counting the number of usage recommendations for author and reviewer guidelines separately. Regression models examined the relationship of journal characteristics with the coverage and type of recommendations of GAI usage guidelines.

resultsA higher proportion of top SJR ranked journals provided author guidelines compared to the random sample of non-top SJR ranked journals (95.0% vs. 86.7%, P < 0.01). The two groups of journals had the same median of 5 on a scale of 0 to 7 for author guidelines and a median of 1 on a scale of 0 to 2 for reviewer guidelines. However, both groups had lower percentages of journals providing recommendations for data analysis and interpretation, with the random sample of non-top SJR ranked journals having a significantly lower percentage (32.5% vs. 16.7%, P < 0.05). A higher SJR score was positively associated with providing GAI usage guidelines for both authors (all P < 0.01) and reviewers (all P < 0.01) among the random sample of non-top SJR ranked journals.

conclusionsAlthough most medical journals provided their own GAI usage guidelines or referenced external guidelines, some recommendations remained unspecified (e.g., whether AI can be used for data analysis and interpretation). Additionally, journals with lower SJR scores were less likely to provide guidelines, indicating a potential gap that warrants attention. Collaborative efforts are needed to develop specific recommendations that better guide authors and reviewers.

Indexed as

Artificial IntelligenceGuidelines as TopicPeriodicals as TopicPublishingCross-Sectional StudiesGenerative Artificial IntelligenceHumansCoverage of recommendationsGAI usage guidelinesMedical journalsScholarly publishingSJR scoreType of recommendations

Identifiers

PMID39934830
PMCPMC11816781

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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.