ArticleCureus2025
Can ChatGPT Recognize Its Own Writing in Scientific Abstracts?
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- How ChatGPT writes scientific titles in medical research: structural and content differences compared to human authors.Journal of the Medical Library Association : JMLA · 2026Article
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
backgroundWith the growing use of generative AI in scientific writing, distinguishing between AI-generated and human-authored content has become a pressing challenge. It remains unclear whether ChatGPT (OpenAI, San Francisco, CA) can accurately and consistently recognize its own output.
methodsWe randomly selected 100 research articles published in 2000, before the advent of generative AI, from 10 high-impact internal medicine journals. For each article, a structured abstract was generated using ChatGPT-4.0 based on the full PDF. The original and AI-generated abstracts (n = 200) were then evaluated twice by ChatGPT-4.0, which was asked to rate the likelihood of authorship on a 0-10 scale (0 = definitely human, 10 = definitely ChatGPT, 5 = undetermined). Classifications of 0-4 were considered human, and 6-10 were considered AI generated.
resultsMisclassification rates were high in both rounds (49% and 47.5%). No abstract received a score of 5. Score distributions overlapped substantially between groups, with no statistically significant difference (Wilcoxon p-value = 0.93 and 0.21). Cohen's kappa for binary classification was 0.33 (95% CI: 0.19-0.46) and weighted kappa on the 0-10 scale was 0.24 (95% CI: 0.15-0.34), both reflecting poor agreement.
conclusionChatGPT-4.0 cannot reliably identify whether a scientific abstract was written by itself or by humans. More robust external tools are needed to ensure transparency in academic authorship.
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.