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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)

The final, peer-reviewed published version of this preprint can be found here:

Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis

Hakam H, Prill R, Lettner J, Korte L, Lovreković B, Ostojic M, Mühlensiepen F

Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis

JMIR Form Res 2024;8:e52164

DOI: 10.2196/52164

PMID: 38363631

PMCID: 10907945

Human-Written vs AI-Generated Texts in Orthopaedic Academic Literature, a Comparative Qualitative Analysis

  • Hassan Hakam; 
  • Robert Prill; 
  • Jonathan Lettner; 
  • Lisa Korte; 
  • Bruno Lovreković; 
  • Marko Ostojic; 
  • Felix Mühlensiepen

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

Please cite as:

Hakam H, Prill R, Lettner J, Korte L, Lovreković B, Ostojic M, Mühlensiepen F

Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis

JMIR Form Res 2024;8:e52164

DOI: 10.2196/52164

PMID: 38363631

PMCID: 10907945

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