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

透明度(行为) 学术诚信 工程伦理学 读写能力 医学 公共关系 高等教育 政治学 开放的体验 公共卫生 研究伦理 学术写作 包裹体(矿物)
作者
Anqi Lin,Zuwei Chen,Aimin Jiang,Bufu Tang,Chang Qi,Lingxuan Zhu,Weiming Mou,Wenyi Gan,Dongqiang Zeng,Mingjia Xiao,Guangdi Chu,Shengkun Peng,Hank Z. H. Wong,Lin Zhang,Hengguo Zhang,Xinpei Deng,Quan Cheng,Jian Zhang,Peng Luo
出处
期刊:International Journal of Surgery [Wolters Kluwer]
卷期号:112 (2): 4418-4433 被引量:2
标识
DOI:10.1097/js9.0000000000003839
摘要

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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