ReviewJournal of educational evaluation for health professions2025
The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors.
Review in Journal of educational evaluation for health professions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Large language models as internal consistency checkers for ophthalmic manuscripts.Eye (London, England) · 2026Article
- AI will reorganize science. Will research remain a human enterprise?Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI in peer review: the elephant in the editorial room.Evidence-based dentistry · 2026Article
- A proof-of-concept study on the use of large language models for assessing research methodology in neuroimaging.Neuroscience informatics · 2026Article
- ChatGPT-4.0 as a Tool for Automated Review of Ethics and Transparency in Biomedical Literature.Journal of Korean medical science · 2026Article
- Evaluating large language models for abstract evaluation tasks: an empirical study.Frontiers in research metrics and analytics · 2026Article
- Variability among large language models in assessing CONSORT compliance of published randomized clinical trials.PloS one · 2026Article
- Role of Medical Editors in the Age of Generative Artificial Intelligence.Healthcare informatics research · 2025Article
- Regulating the unseen hand: AI, authorship, and trust in medical science.Annals of medicine and surgery (2012) · 2025Article
- Artificial Intelligence and Peer Review: Preserving Integrity in the Pursuit of Efficiency.International braz j urol : official journal of the Brazilian Society of UrologyArticle
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
Abstract
The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.
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.