Evaluation of the impact of large language learning models on articles submitted to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase in the use of artificial intelligence in 2023

Abstract

IntroductionThere has been an unprecedented rise is the use of artificial intelligence (AI) amongst medical fields. Recently, a dialogue agent called ChatGPT (Generative Pre-trained Transformer) has grown in popularity through its use of large language models (LLM) to clearly and precisely generate text on demand. However, the impact of AI on the creation of scientific articles is remains unknown. A retrospective study was carried out with the aim of answering the following questions: identify the presence of text generated by LLM before and after the increased usage of ChatGPT in articles submitted in OTSR; determine if the type of article, the year of submission, and the country of origin, influenced the proportion of text generated, at least in part by AI. Material and methodsA total of 390 English articles were submitted to OTSR in January, February and March 2022 (n=204) and over the same months of 2023 (n=186) were analyzed. All articles were analyzed using the ZeroGPT tool, which provides an assumed rate of AI use expressed as a percentage. A comparison of the average rate of AI use was carried out between the articles submitted in 2022 and 2023. This comparison was repeated keeping only the articles with the highest percentage of suspected AI use (greater than 10 and 20%). A secondary analysis was carried out to identify risk factors for AI use. ResultsThe average percentage of suspected LLM use in the entire cohort was 11%±6, with 160 articles (41.0%) having a suspected AI rate greater than 10% and 61 (15.6%) with an assumed AI rate greater than 20%. A comparison between articles submitted in 2022 and 2023 revealed a significant increase in the use of these tools after the launch of ChatGPT 3.5 (9.4% in 2022 and 12.6% in 2023 [p=0.004]). The number of articles with suspected AI rates of greater than 10 and 20% were significantly higher in 2023: >10%: 71 articles (34.8%) versus 89 articles (47.8%) (p=0.008) and >20%: 21 articles (10.3%) versus 40 articles (21.5%) (p=0.002). A risk factor analysis for LLLM use, demonstrated that authors of Asian geographic origin, and the submission year 2023 were associated with a higher rate of suspected AI use. An AI rate >20% was associated to Asian geographical origin with an odds ratio of 1.79 (95% CI: 1.03–3.11) (p=0.029), while the year of submission being 2023 had an odds ratio of 1.7 (95% CI: 1.1–2.5) (p=0.02). ConclusionThis study highlights a significant increase in the use of LLM in the writing of articles submitted to the OTSR journal after the launch of ChatGPT 3.5. The increasing use of these models raises questions about originality and plagiarism in scientific research. AI offers creative opportunities but also raises ethical and methodological challenges. Level of evidenceIII; case control study.