Navigating academic integrity in biomedical research: the impact of large language models on current practices and future directions

As large language models (LLMs) continue to advance, they have garnered widespread public attention and extensive application across numerous industries and academic disciplines. The proliferation of LLMs has sparked considerable research interest, with studies primarily focusing on their technical characteristics and specific application scenarios. However, systematic research examining the impact of LLMs on academic integrity remains relatively scarce. Academic integrity is of paramount importance in the biomedical field. Therefore, this paper aims to examine both the opportunities and challenges that LLMs present to academic integrity in the biomedical field, and proposes solutions for optimizing the beneficial applications of LLMs. From a positive perspective, LLMs offer substantial benefits to researchers by enhancing research efficiency, improving research quality, and facilitating the generation and dissemination of academic insights. However, they also present numerous challenges, including the potential for promoting academic misconduct, generating content inaccuracies or ambiguous expressions, introducing bias and fairness concerns, compromising peer review mechanisms, facilitating the dissemination of misinformation, and undermining higher education - all of which demand careful attention. To address these issues, we propose solutions and feasible strategies centered on ten core dimensions: establishing policies and regulatory guidelines, enhancing AI literacy and application capabilities, developing and improving relevant technical tools, establishing human-AI collaboration models, reforming peer review procedures and academic evaluation systems, promoting international cooperation and standardization, increasing transparency and strengthening disclosure, reinforcing professional ethics education, and advancing artificial intelligence detection technologies. Overall, while LLMs undoubtedly pose challenges for maintaining academic integrity, their potential for positive impact remains promising. It is anticipated that with technological advancement and improved ethical standards, LLMs will ultimately preserve and strengthen academic integrity.

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Publication Details

Journal
International Journal of Surgery
Published
2025-11-21
DOI
https://doi.org/10.1097/js9.0000000000003839
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
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article

Navigating academic integrity in biomedical research: the impact of large language models on current practices and future directions

Shengkun Peng, Bufu Tang, Wenyi Gan, Hengguo Zhang et al.
International Journal of Surgery
Academic integrity and plagiarism
article

Navigating academic integrity in biomedical research: the impact of large language models on current practices and future directions

Shengkun Peng, Bufu Tang, Wenyi Gan, Hengguo Zhang, Dongqiang Zeng, Xinpei Deng, Zuwei Chen, Lin Zhang, Hank Z. H. Wong, Peng Luo, Mingjia Xiao, Weiming Mou, Guangdi Chu, Aimin Jiang, Chang Qi, Jian Zhang, Anqi Lin, Lingxuan Zhu
article en

Abstract

As large language models (LLMs) continue to advance, they have garnered widespread public attention and extensive application across numerous industries and academic disciplines. The proliferation of LLMs has sparked considerable research interest, with studies primarily focusing on their technical characteristics and specific application scenarios. However, systematic research examining the impact of LLMs on academic integrity remains relatively scarce. Academic integrity is of paramount importance in the biomedical field. Therefore, this paper aims to examine both the opportunities and challenges that LLMs present to academic integrity in the biomedical field, and proposes solutions for optimizing the beneficial applications of LLMs. From a positive perspective, LLMs offer substantial benefits to researchers by enhancing research efficiency, improving research quality, and facilitating the generation and dissemination of academic insights. However, they also present numerous challenges, including the potential for promoting academic misconduct, generating content inaccuracies or ambiguous expressions, introducing bias and fairness concerns, compromising peer review mechanisms, facilitating the dissemination of misinformation, and undermining higher education - all of which demand careful attention. To address these issues, we propose solutions and feasible strategies centered on ten core dimensions: establishing policies and regulatory guidelines, enhancing AI literacy and application capabilities, developing and improving relevant technical tools, establishing human-AI collaboration models, reforming peer review procedures and academic evaluation systems, promoting international cooperation and standardization, increasing transparency and strengthening disclosure, reinforcing professional ethics education, and advancing artificial intelligence detection technologies. Overall, while LLMs undoubtedly pose challenges for maintaining academic integrity, their potential for positive impact remains promising. It is anticipated that with technological advancement and improved ethical standards, LLMs will ultimately preserve and strengthen academic integrity.

International Journal of SurgeryVol. 112(2)
Qingdao University (CN), Central South University (CN), TU Wien (AT), University of Electronic Science and Technology of China (CN), Sun Yat-sen University (CN), Jinan University (CN), Chinese University of Hong Kong (HK), Second Military Medical University (CN), Shanghai Jiao Tong University (CN), Anhui Medical University (CN), Fudan University (CN), Quzhou City People's Hospital (CN), Nanfang Hospital (CN), Zhuhai People's Hospital (CN), Changhai Hospital (CN), Affiliated Hospital of Qingdao University (CN), Zhujiang Hospital (CN), Quzhou University (CN), Suzhou Research Institute (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Shanghai First People's Hospital (CN), Sun Yat-sen University Cancer Center (CN), Xiangya Hospital Central South University (CN), Monash University (AU), Southern Medical University (CN), University of Hong Kong (HK), South China University of Technology (CN)
Openalex Percentile: Top 60%
Academic integrity and plagiarism
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