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AI Writing Vocabulary in Ophthalmology: Faster Non-Native Adoption, Unchanged Outcomes
Alessandro Berni1, Daniel Shu Wei Ting2, Greta Caputo3
1From the Department of Neurosciences, Psychology (A.B., G.C., A.R., G.V., F.G., D.B.), Drug Research, and Child Health, Eye Clinic, University of Florence, AOU Careggi, Florence, Italy; Singapore Eye Research Institute (A.B., D.S.W.T., L.G.S.), Singapore National Eye Centre, Singapore.
Purpose:
To quantify the change in large language model (LLM)-associated writing vocabulary in ophthalmology after ChatGPT, to test whether it differed by first-author affiliation-country language group, and to determine whether publishing outcomes shifted.
Design:
Retrospective, cross-sectional bibliometric study, with an interrupted time-series analysis of a 10-year publication census.
Subjects, Participants, And/Or Controls:
Published articles, not human subjects. Primary corpus, 15,683 PubMed abstracts from the 40 highest-impact ophthalmology journals (top 10 per 2025 Journal Citation Reports quartile), pre-ChatGPT (2018-2019) versus post-ChatGPT (2023-2024); supportive corpus, 6139 open-access full texts; and a 48,468-article census (2015-2024) for outcomes. Articles were grouped by whether the first author's affiliation country was native-English-speaking.
Methods, Intervention, Or Testing:
We counted 41 curated LLM-associated excess words per document, with the word count as a Poisson offset and frequency-common control words for specificity. The prespecified primary analysis was the period-by-language-group interaction in a Poisson generalized estimating equation clustered on first author. The instrument was construct-validated against 30 LLM-generated abstracts and against pre-ChatGPT, definitionally LLM-free, human abstracts.
Main Outcome Measures:
The period-by-language-group interaction incidence rate ratio (IRR) for the excess-word rate; and the non-native share of publications and of top-quartile journal placements.
Results:
The excess-word rate rose 2.1-fold, from 320 to 668 per million words. The rise was steeper for non-native-English-affiliated first authors (interaction IRR, 0.61; 95% CI, 0.48-0.78; P < .001), robust to adjustment for journal quartile and country income, to excluding China (IRR, 0.65), and in an independent full-text corpus (IRR, 0.64). The counter separated LLM-generated from human abstracts (area under the receiver operating characteristic curve [AUC], 0.88), whereas per-article discrimination was near chance (AUC, 0.53), confirming a population-level rate rather than a classifier. An interrupted time series showed no post-ChatGPT change in the non-native share of publications or of top-quartile journals.
Conclusions:
After ChatGPT, non-native-English-affiliated authors in ophthalmology adopted AI-associated writing vocabulary faster than native-affiliated authors, without any accompanying gain in publication frequency or journal placement. The measure reflects population-level AI-associated style, not fluency, quality, or confirmed AI use, and should not be used to classify individual articles.
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