Evidence map›Paper›PMID 42521268›Full record

ReviewJournal of Korean medical science2026

Comprehensive Consideration of Ethics in AI-assisted Scientific Writing and Peer Review.

Jin-Hong Yoo

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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jin-Hong YooDivision of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea. jhyoo@catholic.ac.kr.ORCID https://orcid.org/0000-0003-2611-3399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of large language models and generative artificial intelligence (AI) is driving fundamental transformations in the ecosystem of scholarly publishing and peer review. As manuscript production enters an era of sophisticated technological assistance, it has become imperative to transition from traditional approaches focused on misconduct prevention toward a more proactive ethical framework. We propose a new standard centered on transparency, accountability, and confidentiality, presenting a clear solution to challenges associated with integrating AI into academic discourse. Regarding authorship, AI cannot be credited as an author; the final accountability for academic integrity lies only with human authors. Transparency is maintained through a tiered disclosure framework that mandates reporting based on the extent of artificial intelligence utilization. In the context of peer review, while the potential of artificial intelligence to optimize efficiency is recognized, its application must be restricted to a closed security system to safeguard against data breaches. Furthermore, this review highlights new risk factors such as algorithmic sycophancy and prompt injection attacks, emphasizing that a final verification through human expertise is essential to ensuring the integrity of the peer review process. In conclusion, we present a comprehensive regulatory revision roadmap integrating the authors' obligations for transparent information disclosures, reviewers' commitment to confidentiality and security, and editors' ethical oversight. This framework does not regard AI as an object of absolute prohibition but rather positions it as an advanced scholarly aid rooted in human intellectual accountability. The perspectives in this

Indexed as

Artificial IntelligencePeer Review, ResearchWritingAuthorshipConfidentialityGenerative Artificial IntelligenceHumansLarge Language ModelsAIArtificial IntelligenceLarge Language Model (LLM)

Identifiers

PMID42521268
PMCPMC13406946

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.