Navigating academic integrity in biomedical research: The impact of large language models on current practices and future directions
FAR Publishing Limited
image:
Large language models are predominantly applied in four major academic domains: (1) Research Paper Writing Assistance: LLMs facilitate manuscript preparation through translation services, comprehensive proofreading, and multiple perspective analysis, while simultaneously managing auxiliary tasks such as data organization, thereby substantially enhancing research productivity. (2) Research Data Analysis Support: LLMs exhibit emerging capabilities in dataset compilation, analytical processing, and automated code generation, leading to significant improvements in research efficiency. (3) Academic Visualization Generation: LLMs enhance the traditional visualization process through automated generation of scientific illustrations and data representations, facilitating more effective communication of research findings. (4) Academic Peer Review Support: LLMs contribute to both manuscript self-assessment and formal review processes by providing systematic content evaluation and generating structured review feedback.This figure was created based on the tools provided by Biorender.com (accessed on 20/08/2025)
view moreCredit: 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
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.
Journal
International Journal of Surgery
Method of Research
Literature review
Article Title
Navigating Academic Integrity in Biomedical Research: The Impact of Large Language Models on Current Practices and Future Directions
No comments:
Post a Comment