ReviewJournal of Korean medical science2025
Perspectives of Artificial Intelligence Use for In-House Ethics Checks of Journal Submissions.
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Artificial Intelligence as a Safeguard for Clinical Scientific Integrity: A Human-AI Hybrid Model for Medical Peer Review.Journal of clinical medicine · 2026Article
- Variability among large language models in assessing CONSORT compliance of published randomized clinical trials.PloS one · 2026Article
- Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors.Journal of Korean medical science · 2025Review
- Ethical Use of Artificial Intelligence for Processing Medical Images.Journal of Korean medical science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.