Evidence map›Paper›PMID 41250039›Full record

ArticleBMC primary care2025

Full title: evaluating AI guidelines in leading family medicine journals: a cross-sectional study.

Cameron O'Brien, Zohaib Thayani, Tim Smith, Andrew V Tran, Patrick Crotty, Alec Young, Alicia Ito Ford, Matt Vassar

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC primary care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Cameron O'BrienOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA. obriencameron99@gmail.com.
Zohaib ThayaniOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Tim SmithOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Andrew V TranOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Patrick CrottyOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Alec YoungOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Alicia Ito FordOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.
Matt VassarOffice of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly integrated into family medicine research and practice, enhancing diagnostics, data analysis, and care delivery. Yet, its rapid adoption has outpaced the development of standardized editorial policies, raising concerns about transparency, ethics, and reproducibility. Clear guidance from journals is urgently needed to ensure responsible use of AI in research and publishing.

objectiveTo evaluate editorial policies and reporting guideline endorsements related to AI across leading FM journals.

methodsUsing the SCImago Journal Rank database, we conducted a cross-sectional analysis of FM journals. From November 2024 to January 2025, we reviewed publicly available Instructions for Authors for AI-related policies, including authorship, manuscript writing, content/image generation, and disclosure. We also assessed whether journals endorsed AI-specific RGs (e.g., CONSORT-AI, SPIRIT-AI). Data were extracted in duplicate using a standardized form. Reproducibility was supported through protocol registration on Open Science Framework.

resultsOf 57 FM journals identified, 40 met inclusion criteria. Among these, 82.5% (33/40) referenced AI in their policies. Most (77.5%) prohibited AI authorship and required disclosure of AI use, while 72.5% permitted AI-assisted manuscript writing. Policies on AI-generated content and images varied, with 47.5% and 50.0% of journals allowing their use, respectively. Only 5.0% (2/40) endorsed AI-specific RGs. No correlation was observed between journal characteristics and AI policy adoption.

conclusionsMost family medicine journals now address AI use, but notable gaps remain, particularly in endorsing AI-specific reporting guidelines. Without broader adoption of structured guidance, AI-integrated research risks inconsistency, limited reproducibility, and ethical challenges. Strengthening journal policies and endorsing standardized reporting frameworks is essential to ensure high-quality, trustworthy AI research in family medicine.

Indexed as

Artificial IntelligenceEditorial PoliciesFamily PracticeGuidelines as TopicPeriodicals as TopicAuthorshipCross-Sectional StudiesHumansReproducibility of ResultsAI journal policiesArtificial intelligenceEditorial policiesFamily medicineReporting guidelines

Identifiers

PMID41250039
PMCPMC12625767

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.