The Use of Large Language Models and Their Association With Enhanced Impact in Biomedical Research and Beyond

• Published in: MedComm - Future medicine Vol. 4; no. 2
• Main Authors: Cheng, Huzi, Shi, Wen, Sheng, Bin, Lee, Aaron Y., Car, Josip, Chaudhary, Varun, Atanasov, Atanas G., Liu, Nan, Qiu, Yue, Chen, Qingyu, Wong, Tien Yin, Tham, Yih‐Chung, Zheng, Ying‐Feng
• Format: Journal Article
• Language: English
• Published: Wiley 01.06.2025
• Subjects: academic writing; chatGPT; large language models; LLM
• ISSN: 2769-6456; 2769-6456
• DOI: 10.1002/mef2.70019

ABSTRACT The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines. This study analyzed articles from arXiv, bioRxiv, and medRxiv published between 2022 and 2024, using a novel tool for detecting LLM usage. We found that (1) LLMs have been widely adopted since late 2022, and (2) their usage appears to enhance the impact of research papers. Overall, our findings suggest that LLMs may positively associated with scientific communication without altering core research content.