Abstract
Background:
While large language models (LLMs) have made a tremendous impact on scientific workflow, there are also serious concerns about ethical integrity and nondiverse written language in medical and orthopaedic literature. Recent studies have yielded conflicting results regarding the involvement of artificial intelligence (AI) in writing processes.
Purpose:
To (1) determine the extent of AI involvement in
American Journal of Sports Medicine
(AJSM) abstracts across 4 different time periods; (2) to determine whether AI usage differs based on authorship nativity.
Study Design:
Cross-sectional study.
Methods:
We retrospectively reviewed abstracts from AJSM and organized these into 4 periods. We chose the year 2000 as a human baseline to establish a threshold for AI involvement. We also selected the years 2015 and 2020 to observe changes in AI probability scores. Considering the release of ChatGPT in November 2022, we included abstracts from 2023 to 2025 to determine the increase in AI usage. We classified articles into native English-speaking, non-native English-speaking, and mixed groups to determine authorship nativity based on institutional affiliations. We used Originality.AI to report AI probability scores. We evaluated differences across years and nativity groups using the Kruskal-Wallis and post hoc tests.
Results:
Across the 6 time periods, 1885 abstracts were analyzed. AI probability scores from the year 2000 (n = 124; mean, 1.68%) were significantly lower than all subsequent years (
P
P
≥ .999). However, scores began a rapid and significant increase starting in 2023 (n = 345; mean 15.48%) and continued to rise through 2025 (n = 323; mean, 30.6%;
P
P
= .364), 2024 (
P
= .053), or 2025 (
P
= .454).
Conclusion:
Our study demonstrated that the prevalence of AI-like linguistic patterns in AJSM abstracts has increased since the release of ChatGPT. AI adoption across all nativity groups suggests that AI has become a universal standard for efficient article writing, regardless of the language barrier encountered by non-native speakers.