The growing presence of AI-generated content in ophthalmology: a retrospective bibliographic analysis
Bibliographic record
Abstract
OBJECTIVE: To quantify the prevalence and trends of artificial intelligence (AI)-generated content in ophthalmology manuscripts, particularly following the public release of OpenAI's ChatGPT on November 30, 2022. METHODS: A retrospective bibliographic analysis was conducted on 1 036 manuscripts from 30 ophthalmology journals, divided into pre-December 2022 (519 manuscripts) and post-December 2022 (517 manuscripts) periods. AI-generated content was evaluated using the Originality Standard 2.0.0 model, which calculates AI probability scores (AIPS) ranging from 0% to 100%. Readability metrics (e.g., Flesch-Kincaid Score) and journal impact metrics (e.g., impact factor) were analyzed. RESULTS: AIPS remained stable from 2014 to 2022 but increased significantly after December 2022 (p < 0.001). The mean AIPS rose from 4.95% in 2022 to 11.2% by mid-2024, with projections estimating 17.51% by mid-2026. Editorials exhibited the highest mean AIPS (12.8%), while surgical technique studies had the lowest (4.33%). Higher AIPS were associated with lower journal impact factors (Spearman's ρ = -0.54; p < 0.001) and simpler language, as reflected by lower Automated Readability Index scores (Spearman's ρ = -0.12; p < 0.005). None of the included manuscripts disclosed AI usage, including 44 manuscripts with AIPS exceeding 25%. CONCLUSIONS: AI-generated content in ophthalmology has risen significantly since ChatGPT's release. Higher AIPS correlates with lower journal impact factors and reduced literary complexity. The lack of AI usage disclosure raises ethical concerns and emphasizes the need for transparent reporting and guidelines to ensure the integrity of scientific research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".