Independent detection approaches in different medical fields converge on the same pattern: the corpus-wide excess-vocabulary method of Kobak et al. 2025 and the journal-level AI-detection screening of Fortenbach et al. 2026 both document a sharp post-ChatGPT rise in LLM-generated text, with Fortenbach et al. finding over a quarter of sampled ophthalmology research articles showing outlier AI-likelihood scores by 2025.
E1 documents an abrupt post-ChatGPT rise via excess vocabulary, and E17 documents a marked post-ChatGPT increase with 25.7% (over a quarter) of sampled research articles showing outlier (>2 SD) AI-likelihood scores by 2025.
Written by Kimi K3 via Ollama Cloud · 30 Sept, 22:48
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“at least 13.5% of 2024 abstracts were processed with LLMs”
Full-text passage · no page number · evidence E1
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 02
“By 2025, 25.7% of sampled research articles and 21.6% of commentary articles contained AI-likelihood scores of more than 2 standard deviations above the baseline”
Abstract · no page number · evidence E17