ArticleJournal of Korean medical science2026
ChatGPT-4.0 as a Tool for Automated Review of Ethics and Transparency in Biomedical Literature.
Article 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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of artificial intelligence, specifically large language models, into editorial processes, is gaining interest due to its potential to streamline manuscript assessments, particularly regarding ethical and transparency reporting in public health journals. This study aims to evaluate the capability and limitations of ChatGPT-4.0 in accurately detecting missing ethical and transparency statements in research articles published in high-ranked (Q1) versus low-ranked (Q4) public health journals.
methodsArticles from top-tier (Q1) and low-tier (Q4) public health journals were analyzed using ChatGPT-4.0 for the presence of essential ethical components, including ethics approval, informed consent, animal ethics, conflicts of interest, funding notes, and open data sharing statements. Performance metrics such as sensitivity, recall, and precision were calculated.
resultsChatGPT exhibited high sensitivity and recall across all evaluated components, accurately identifying all missing ethics statements. However, precision varied significantly between categories, with notably high precision for data availability statements (0.96) and significantly lower precision for funding statements (0.16). A comparative analysis between Q1 and Q4 journals showed a marked increase in missing ethics statements in the Q4 group, particularly for open data sharing statements (4 vs. 50 cases), ethics approval (2 vs. 5 cases), and informed consent statements (3 vs. 8 cases).
conclusionChatGPT-4.0 in preliminary screening shows considerable promise, providing high accuracy in identifying missing ethics statements. However, limitations regarding precision highlight the necessity for additional human checks. A balanced integration of artificial intelligence and human judgment is recommended to enhance editorial checks and maintain ethical standards in public health publishing.
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.