Accepted for/Published in: JMIR Formative Research
Date Submitted: Aug 24, 2023
Open Peer Review Period: Aug 24, 2023 - Oct 19, 2023
Date Accepted: Dec 13, 2023
(closed for review but you can still tweet)
Human-Written vs AI-Generated Texts in Orthopaedic Academic Literature, a Comparative Qualitative Analysis
ABSTRACT
Background:
As language learning models are becoming increasingly integrated into different aspects of health care, questions about its implication on medical research began to emerge. Key aspects such as authenticity in academic writing are at stake with AI generating highly accurate and grammatically sound texts.
Objective:
The objective of this study is to contrast human written with AI-generated scientific literature in orthopaedics and sports medicine.
Methods:
For this purpose, five human-written abstracts were selected from an online medical database and rewritten with the assistance of artificial intelligence. Abstracts entirely generated by AI were subsequently included and all the abstracts dealt with meniscal injuries. Criteria suggested by previous research for the purpose of identifying AI generated texts was then presented with each article. After randomizing the order of all abstracts, researchers were asked to evaluate the texts according to these criteria and provide feedback on whether the texts were human-written, or AI generated. The abstracts were then run through AI detection software.
Results:
Neither the researchers nor the AI-detection software could successfully identify the AI-generated texts. Furthermore, the criteria previously suggested in the literature did not correlate on whether the researchers deemed a text to be AI-generated or whether they judged the article correctly based on these parameters.
Conclusions:
Due to the small sample size, it is not possible to generalize the results of this study. As is the case with any tool used in academic research, the potential to cause harm can be mitigated by relying on the transparency and integrity of the researchers. However, the inability of experienced researchers to correctly identify AI-generated texts is a powerful conclusion of this study. With scientific integrity at stake, further research with a similar study design should be conducted to determine the magnitude of this issue. Clinical Trial: Due to the noninterventional study design, trial registration was not deemed necessary.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.