Detectable AI-Generated Text in the Journal of Craniofacial Surgery : A Comparison of 2014 and 2024 Publications

The rapid emergence of large language models (LLMs) has transformed scientific writing, prompting concerns regarding the extent to which generative artificial intelligence (AI) tools may be influencing published research manuscripts. AI-detection software has been proposed as a method to identify AI-generated text; however, the validity of these tools in scientific contexts remains uncertain. This study evaluates detectable AI content in articles published in the Journal of Craniofacial Surgery (JCFS) before and after the widespread adoption of LLMs.

A retrospective cross-sectional analysis was conducted using all JCFS articles published in 2014 (pre-LLM) and 2024 (post-LLM). Full-text manuscripts and individual sections (Abstract, Introduction, Methods, Results, Discussion, Conclusion) were analyzed using ZeroGPT to determine the percentage of detectable AI-generated text. Detection scores were compared using the Mann-Whitney U test.

A total of 1490 manuscripts were analyzed (659 in 2014; 831 in 2024). Mean detectable AI content increased from 8.6% (SD 9.8) in 2014 to 10.7% (SD 10.4) in 2024 ( P = 0.00001). Section-level comparison demonstrated the greatest increase in Results sections (19.8%-24.1%, P = 0.00001), with additional increases in Introduction, Methods, Discussion, and Conclusion sections, but no significant change in Abstracts (13.4% versus13.9%, P = 0.32). Although statistically significant, these differences were small in absolute magnitude.

Detectable AI content in JCFS manuscripts increased modestly over the past decade, likely reflecting detection software behavior and evolving writing structure rather than widespread use of generative AI. Findings support cautious interpretation of AI-detection outputs and highlight the need for validated tools and thoughtful editorial policy development.