Evidence map›Paper›PMID 40461144›Full record

ReviewJournal of Korean medical science2025

Perspectives of Artificial Intelligence Use for In-House Ethics Checks of Journal Submissions.

Fatima Alnaimat, Abdel Rahman Feras AlSamhori, Omar Hamdan, Birzhan Seiil, Ainur B Qumar

Abstract readReview
In one paragraph

Review in Journal of Korean medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

5 authors.

Fatima AlnaimatDivision of Rheumatology, Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan. f.naimat@ju.edu.jo.ORCID https://orcid.org/0000-0002-5574-2939
Abdel Rahman Feras AlSamhoriSchool of Medicine, University of Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0002-2715-4320
Omar HamdanSchool of Medicine, University of Jordan, Amman, Jordan.ORCID https://orcid.org/0009-0008-0442-0578
Birzhan SeiilDepartment of Chemical Disciplines, Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.ORCID https://orcid.org/0000-0003-1524-8888
Ainur B QumarDepartment of Health Policy and Management, Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan.ORCID https://orcid.org/0000-0003-0457-7205

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has shown its ability to transform academic writing and publishing. It offers significant benefits, including enhancing efficiency, consistency, and integrity, However, these advancements are accompanied by ethical concerns (particularly around authorship, originality, and transparency) and the need for human oversight in peer review and editorial processes. In this study we explore AI for ethics checks in journal submissions. Specific AI platforms-such as YesChat for bias detection, Turnitin's iThenticate for plagiarism, Proofig for image integrity, and GPTZero for AI-generated content-can identify ethical breaches through tailored prompts and queries. Additionally, AI is increasingly used to detect missing or vague ethics statements, conflicts of interest, and citation manipulation by analyzing structured text and databases. AI-enhanced tools like Elsevier's Editorial Manager and Enago Read assist in ensuring compliance with journal-specific ethical guidelines and streamline peer review. Moreover, emerging algorithms, such as CIDRE, have shown promise in identifying abnormal citation behaviors. As AI accuracy improves, these platforms are expected to be integrated directly into submission systems, enhancing research integrity, transparency, and accountability.

Indexed as

Artificial IntelligencePeriodicals as TopicAlgorithmsAuthorshipHumansPeer Review, ResearchPlagiarismPublishingAcademic PublishingArtificial IntelligenceAuthorshipEthicsPeer Review

Identifiers

PMID40461144
PMCPMC12133599

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.