ArticlePerspectives on medical education2025
Delving Into PubMed Records: How AI-Influenced Vocabulary has Transformed Medical Writing since ChatGPT.
Article in Perspectives on medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
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
Introduction: It is estimated that large language models (LLMs), including ChatGPT, are already widely used in academic paper writing. This study examined whether certain words and phrases reported as frequently used by LLMs have increased in medical literature, comparing their trends with common academic expressions. Methods: A structured literature review identified 135 potentially AI-influenced terms from 15 studies documenting LLM vocabulary patterns. For comparison, 84 common academic phrases in medical research served as controls. PubMed records from 2000 to 2024 were analyzed to track the frequency of these terms. Usage trends were normalized using a modified Z-score transformation. Results: Of the 135 potentially AI-influenced terms, 103 showed meaningful increases (modified Z-score ≥3.5) in 2024. Terms with the highest increases included "delve," "underscore," "primarily," "meticulous," and "boast." The linear mixed-effects model revealed significantly higher usage of potentially AI-influenced terms compared to controls (β = 0.655, p < 0.001). Notably, these terms began increasing in 2020, preceding ChatGPT's 2022 release, with marked acceleration in 2023-2024. Discussion: Certain words and phrases have become more common in medical literature since ChatGPT's introduction. However, the use of these terms tended to increase before 2022, indicating the possibility that the emergence of LLMs amplified existing trends rather than creating entirely new patterns. By understanding which terms are overused by AI, medical educators and researchers can promote better editing of AI-assisted drafts and maintain diverse vocabulary across scientific writing.
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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.