Lexical Diffusion: Linguistic Characteristics of Ophthalmology Science Articles with Disclosed Artificial Intelligence-Assisted Writing.
- 1Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Abstract
Objective
To characterize discussion-section language in ophthalmology research articles with disclosed generative artificial intelligence (AI) writing assistance and to examine temporal lexical patterns among articles without disclosure.Design
Cross-sectional observational study.Subjects
Original research articles published in Ophthalmology Science from January 2024 through May 2026.Methods
Articles were classified according to disclosure of generative AI writing assistance. Discussion-section text was analyzed for Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), predefined generative AI-associated terms, control academic terms, and their ratio. Articles with disclosed AI writing assistance were compared with nondisclosing articles using repeated stratified 1:5 resampling by publication year and first-author English-language-dominant affiliation status across 1000 iterations. Temporal trends were evaluated among nondisclosing articles.Main outcome measures
Flesch Reading Ease, FKGL, SMOG, term densities, and AI-associated-to-control term ratio.Results
Among 633 articles, 28 (4.4%) disclosed generative AI writing assistance. English-language-dominant affiliation fractions were lower among AI-disclosed articles for first and corresponding authors (P = 0.008 and 0.012, respectively). Artificial intelligence-disclosed articles had lower FRE (11.97 vs. 21.59), higher FKGL (17.15 vs. 16.00), and higher SMOG scores (17.82 vs. 17.12; P values < 0.001, 0.013, and 0.027, respectively). Resampling produced mean Hodges-Lehmann shifts of -7.35 for FRE (95% resampling interval, -8.90 to -5.83), 1.06 for FKGL (0.80-1.32), and 0.66 for SMOG (0.47-0.86). Artificial intelligence-associated term density and the AI-associated-to-control term ratio were higher in AI-disclosed articles (22.38 vs. 15.71 terms per 1000 words and 0.91 vs. 0.63; P = 0.003 and 0.004, respectively), whereas control-term density did not differ (P = 0.844); resampling confirmed these lexical findings. Among nondisclosing articles, AI-associated term density and the ratio increased over publication years (all, P < 0.001), whereas control-term density did not (all, P > 0.05).Conclusions
Disclosed generative AI writing assistance was uncommon but was associated with lower formula-based readability, higher estimated reading grade levels, and a higher density of predefined generative AI-associated vocabulary in discussion sections. The temporal pattern among nondisclosing articles also suggests possible corpus-level diffusion of AI-associated lexical patterns, although disclosure status does not verify actual AI use.Financial disclosures
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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BibTeX
@article{inooka2026lexical,
title = {{Lexical Diffusion: Linguistic Characteristics of Ophthalmology Science Articles with Disclosed Artificial Intelligence-Assisted Writing.}},
author = {Taiga Inooka and Kenya Yuki and Koji M Nishiguchi},
journal = {Ophthalmology science},
year = {2026},
month = sep,
doi = {10.1016/j.xops.2026.101418},
url = {https://europepmc.org/article/MED/42927167},
note = {CC BY},
}
RIS
TY - JOUR
TI - Lexical Diffusion: Linguistic Characteristics of Ophthalmology Science Articles with Disclosed Artificial Intelligence-Assisted Writing.
AU - Taiga Inooka
AU - Kenya Yuki
AU - Koji M Nishiguchi
PY - 2026
DA - 2026/09/21
JO - Ophthalmology science
DO - 10.1016/j.xops.2026.101418
UR - https://europepmc.org/article/MED/42927167
AB - Objective To characterize discussion-section language in ophthalmology research articles with disclosed generative artificial intelligence (AI) writing assistance and to examine temporal lexical patterns among articles without disclosure.Design Cross-sectional observational study.Subjects Original research articles published in Ophthalmology Science from January 2024 through May 2026.Methods Articles were classified according to disclosure of generative AI writing assistance. Discussion-section text was analyzed for Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), predefined generative AI-associated terms, control academic terms, and their ratio. Articles with disclosed AI writing assistance were compared with nondisclosing articles using repeated stratified 1:5 resampling by publication year and first-author English-language-dominant affiliation status across 1000 iterations. Temporal trends were evaluated among nondisclosing articles.Main outcome measures Flesch Reading Ease, FKGL, SMOG, term densities, and AI-associated-to-control term ratio.Results Among 633 articles, 28 (4.4%) disclosed generative AI writing assistance. English-language-dominant affiliation fractions were lower among AI-disclosed articles for first and corresponding authors (P = 0.008 and 0.012, respectively). Artificial intelligence-disclosed articles had lower FRE (11.97 vs. 21.59), higher FKGL (17.15 vs. 16.00), and higher SMOG scores (17.82 vs. 17.12; P values < 0.001, 0.013, and 0.027, respectively). Resampling produced mean Hodges-Lehmann shifts of -7.35 for FRE (95% resampling interval, -8.90 to -5.83), 1.06 for FKGL (0.80-1.32), and 0.66 for SMOG (0.47-0.86). Artificial intelligence-associated term density and the AI-associated-to-control term ratio were higher in AI-disclosed articles (22.38 vs. 15.71 terms per 1000 words and 0.91 vs. 0.63; P = 0.003 and 0.004, respectively), whereas control-term density did not differ (P = 0.844); resampling confirmed these lexical findings. Among nondisclosing articles, AI-associated term density and the ratio increased over publication years (all, P < 0.001), whereas control-term density did not (all, P > 0.05).Conclusions Disclosed generative AI writing assistance was uncommon but was associated with lower formula-based readability, higher estimated reading grade levels, and a higher density of predefined generative AI-associated vocabulary in discussion sections. The temporal pattern among nondisclosing articles also suggests possible corpus-level diffusion of AI-associated lexical patterns, although disclosure status does not verify actual AI use.Financial disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
SN - 2666-9145
ER -