The Silent Author

Abstract

Background: Academic publishing underpins surgical decision-making, but the rapid adoption of generative artificial intelligence (AI) raises concerns about research credibility and patient safety. To the best of our knowledge, no prior pilot study has examined its presence in plastic and reconstructive surgery. Detection tools remain imperfect, and journals lack consensus on disclosure policies, leaving a gap between rapid adoption and effective oversight. Methods: This pilot analysis sampled 10% (n=67) of articles published between July 1, 2024, and July 1, 2025, across leading plastic and reconstructive surgery and burn journals. We assembled a matched control cohort from 2014 to 2015 using identical criteria. Articles were analyzed using a combined RoBERTa classifier and perplexity-based evaluation to flag potential AI-like textual characteristics. Results: At the article level, 18 of 67 articles (26.9%, 95% CI: 17.7-38.5) contained ≥1 flagged section, with 20 subsections (5.3%, 95% CI: 3.5-8.1) flagged. Flagged content clustered in methods (9.1%) and abstracts (7.5%), with lower prevalence in other sections. In our control cohort, 3 papers (4.5%, 95% CI: 1.5-12.5) and 3 subsections were flagged (0.9%, 95% CI: 0.3-2.7), representing ~6-fold and 5-fold increases, respectively. This difference was statistically significant at the article level (χ²=11.1, P <0.001). Conclusions: AI-like textual characteristics were more frequently detected in contemporary plastic and reconstructive surgery publications than in the pre-AI cohort. Although detection does not confirm authorship, these findings underscore the need for clearer and more consistent disclosure, standardized and graded reporting policies, and reviewer training to enable responsible integration of AI into surgical publishing.

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