Are Open Access Text Analysis Tools Capable of Identifying Writing by Generative Intelligence in Artificial Intelligence Manuscripts in Cephalometry?
DOI:
https://doi.org/10.21270/archi.v13i9.6429Keywords:
Artificial Intelligence, Cephalometry, Plagiarism, Diagnostic ImagingAbstract
Introduction: Due to the rapid development of generative artificial intelligence, it is necessary to study the possibility of detecting this type of writing. For this purpose, there are tools for detecting generative writing, which read documents and seek to identify patterns of writing related to generative artificial intelligence. Objective: To assess the capability of freely accessible text analysis tools to identify generative artificial intelligence writing in manuscripts on artificial intelligence in cephalometrics. Materials and methods: To address the question "Can freely accessible text analysis tools identify generative writing in manuscripts on artificial intelligence in cephalometrics?", a search was conducted on the Pubmed research platform using the PICOS strategy. A total of 62 manuscripts were selected. The analysis of generative writing was evaluated using two freely accessible generative writing detection tools: ZeroGPT and contentdetector.ai. Results were tabulated in a spreadsheet and analyzed using IBM SPSS statistical software. Results: In the analysis of different detection tools, there were no statistically significant differences between different years. However, there was a statistically significant difference when comparing the two detection tools, with average detection percentages higher than 26% for the ContentDetector.AI tool. Conclusion: The detection tools used were not able to correctly detect or agree on the use of generative intelligence in manuscript writing.
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