Evidence map›Paper›PMID 42521272›Full record

ReviewJournal of Korean medical science2026

Toward Structured Transparency: A Governance Framework for AI Use in Biomedical Publishing.

Jong-Min Kim

Abstract readReview
In one paragraph

Review in Journal of Korean medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Review
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

1 author.

Jong-Min KimDepartment of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea. jongmin1@snu.ac.kr.ORCID https://orcid.org/0000-0001-5723-3997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid adoption of large language models and generative artificial intelligence (AI) is transforming biomedical research and publishing. Although international organizations such as the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) have established the principle that AI cannot be recognized as an author, and the ICMJE, in its January 2026 revision, has introduced a dedicated section addressing AI use by authors, peer reviewers, and editors, journal-level Instructions for Authors still require further operational detail to apply these principles consistently throughout the publication process. This review critically examines current AI-related policies in the

Indexed as

Artificial IntelligenceBiomedical ResearchPublishingAuthorshipConflict of InterestEditorial PoliciesGenerative Artificial IntelligenceHumansGenerative Artificial IntelligenceGuidelinesLarge Language ModelsMedical PublishingPeer Review

Identifiers

PMID42521272
PMCPMC13406945

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.