ReviewJournal of Korean medical science2026
Authorship, Disclosures, and Conflicts of Interest in Papers Related to the Use of Artificial Intelligence.
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 2 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
2 citing papers in PubMed.
- Comprehensive Consideration of Ethics in AI-assisted Scientific Writing and Peer Review.Journal of Korean medical science · 2026Review
- Toward Structured Transparency: A Governance Framework for AI Use in Biomedical Publishing.Journal of Korean medical science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As artificial intelligence (AI), particularly generative AI, is being actively introduced and utilized in medical research and manuscript writing, new challenges are emerging in academic publishing, specifically regarding author attribution, transparency, and conflicts of interest. This review examines the current status of AI use in medical publishing by focusing on three key areas: author attribution, disclosure methods regarding AI usage, and conflicts of interest. The prevailing view to date is that AI cannot be recognized as an author because it lacks the capacity to assume the responsibility that is a core requirement of authorship. Therefore, AI contributions are generally disclosed in the acknowledgment section. As AI becomes more deeply involved in the analysis and manuscript writing processes, it is expected that discussions regarding the attribution of intellectual contributions and the boundaries between tools and contributors will become more active in the future. The transparency of reporting AI usage depends on how the AI contributes to the research or manuscript. While AI used for purposes such as grammar or spelling correction is often exempt from disclosure requirements, if AI contributes more substantially to the content of the paper-such as text generation, data analysis, or code development-it is necessary to report this by indicating such details explicitly within the paper. However, stances on the level of disclosure vary among journals, such as whether to reveal all details like model specifications or prompts. Nevertheless, when generative AI is used in the research itself, detailed reporting is increasingly emphasized to ensure the reproducibility and scientific validity of the findings. Furthermore, AI adds a new dimension to conflicts of interest. This includes financial interests related to AI development, data ownership, and potential biases inherent in training datasets and algorithms. Since these factors can influence research results in subtle ways, more transparent and comprehensive disclosure of conflicts of interest in AI-based research is crucial.
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.