====== Ethical guidelines ======

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With the rapid proliferation of [[artificial intelligence tools]], important questions about their [[applicability]] to [[manuscript]] [[preparation]] have been raised. 

Schneider et al. explore the methodological challenges of detecting AI-generated content in neurosurgical [[publication]]s, using existing detection tools to highlight both the presence of AI content and the fundamental limitations of current detection approaches.

They analyzed 100 [[random]]ly selected manuscripts published between 2023 and 2024 in high-impact [[neurosurgery]] [[journals]] using a two-tiered approach to identify potential AI-generated text. The text was classified as AI-generated if both robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa)-based AI classification tool yielded a positive classification and the text's perplexity score was less than 100. Chi-square tests were conducted to assess differences in the prevalence of AI-generated text across various manuscript sections, topics, and types. To eliminate bias introduced by the more structured nature of abstracts, a subgroup analysis was conducted that excluded abstracts as well.

Approximately one in five (20%) manuscripts contained sections flagged as AI-generated. [[Abstract]]s and methods sections were disproportionately identified. After excluding abstracts, the association between section type and AI-generated content was no longer statistically significant.

The findings highlight both the increasing integration of AI in manuscript preparation and a critical challenge in academic publishing as AI language models become increasingly sophisticated and traditional detection methods become less reliable. This suggests the need to shift focus from detection to [[transparency]], emphasizing the [[development]] of clear [[disclosure]] policies and ethical [[guidelines]] for AI use in academic writing
((Schneider DM, Mishra A, Gluski J, Shah H, Ward M, Brown ED, Sciubba DM, Lo SL. Prevalence of Artificial Intelligence-Generated Text in Neurosurgical Publications: Implications for Academic Integrity and Ethical Authorship. Cureus. 2025 Feb 16;17(2):e79086. doi: 10.7759/cureus.79086. PMID: 40109787; PMCID: PMC11920854.)).
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Schneider et al. provide a valuable [[contribution]] to the growing [[literature]] on AI in [[scientific publishing]]. Their work underscores both the increasing pervasiveness of LLMs and the urgent need for [[academic institution]]s, [[journals]], and [[researcher]]s to develop clear norms around AI use. However, methodological and interpretative limitations—particularly the uncertain [[reliability]] of detection tools—temper the strength of their conclusions.

Moving forward, the field may benefit more from transparent [[disclosure]] policies and collaborative development of ethical [[framework]]s than from unreliable attempts at AI detection. As AI becomes more embedded in the scientific process, clarity, not concealment, must be the guiding principle.