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

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.

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Publication Details

Journal
MedComm – Future Medicine
Published
2025-04-27
DOI
https://doi.org/10.1002/mef2.70019
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48

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article

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

Bin Sheng, Yingfeng Zheng, Aaron Lee, Varun Chaudhary et al.
1 citations
MedComm – Future Medicine
Topic Modeling
2.48
article

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

Bin Sheng, Yingfeng Zheng, Aaron Lee, Varun Chaudhary, Yue Qiu, Qingyu Chen, Atanas G. Atanasov, Josip Car, Tien Yin Wong, Yih Chung Tham, Nan Liu, Wen Shi, Huzi Cheng
article en
1 citations

Abstract

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.

MedComm – Future MedicineVol. 4(2)
Sun Yat-sen University (CN), National University of Singapore (SG), Nanyang Technological University (SG), Shanghai Jiao Tong University (CN), University of Washington (US), Singapore National Eye Center (SG), Yale University (US), Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences (PL), First Affiliated Hospital of Guangzhou Medical University (CN), Singapore Eye Research Institute (SG), Indiana University Bloomington (US), Duke-NUS Medical School (SG), Beijing Tsinghua Chang Gung Hospital (CN), Impact (CA), Imperial College London (GB), Medical University of Vienna (AT), Guangzhou Medical University (CN), McMaster University (CA), Tsinghua University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Quality Education
Openalex Percentile: Top 11%
Topic Modeling
2.48
DOIOpen Access PDFOpenAlex
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