ArticleJournal of general internal medicine2026
Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts.
Article in Journal of general internal medicine, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of artificial intelligence (AI) models as reviewers of scientific content raises concerns about potential biases related to author identity and about the reproducibility of their evaluations. We assessed whether AI-based reviewers exhibit gender or geographic bias and evaluated the reproducibility of their scoring of scientific abstracts.
methodsWe randomly selected 10 general internal medicine journals indexed in the Journal Citation Reports (impact factor ≥ 1.5). For each journal, five original research abstracts were retrieved in April 2026 from the Web of Science. Each abstract (n = 50) was assigned to four fictional author identities (African female, African male, American female, American male). Abstracts were independently evaluated twice by two AI models (ChatGPT and Claude), scoring quality/novelty/acceptance on a 0-10 scale, yielding 800 evaluations. Bias was assessed using multivariable ordinal logistic regression, and reproducibility using percent agreement and Fleiss' kappa.
resultsFor ChatGPT, quality and novelty scores were identical across identities (median [IQR] = 7 [1] and 5 [2], respectively), with minimal variation in acceptance (median [IQR] = 6-7 [1-2]). For Claude, all scores were identical across identities (quality = 7 [1], novelty = 5 [2], acceptance = 6 [2]). Multivariable analyses showed no overall association between author identity and scores, with the exception of acceptance for Claude at the global level, although no individual comparisons were statistically significant. Reproducibility was high for both models, with percent agreement > 0.98 and substantial/excellent agreement (κ = 0.74-0.89).
conclusionsIn this controlled setting, we detected no consistent evidence of gender or geographic bias, and reproducibility was high, although the restricted score range may have contributed to the observed agreement. Further studies are needed to assess the validity of LLM-based evaluation and compare it with human peer review.
Indexed as
Identifiers
42827220What 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.