Doskaliuk Bohdana, Zimba Olena, Yessirkepov Marlen, Klishch Iryna, Yatsyshyn Roman
Department of Pathophysiology, Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine.
Department of Rheumatology, Immunology and Internal Medicine, University Hospital in Kraków, Kraków, Poland.
J Korean Med Sci. 2025 Feb 24;40(7):e92. doi: 10.3346/jkms.2025.40.e92.
The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI's potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI's efficiencies in editorial processes.
人工智能（AI）的迅速发展改变了科学研究的各个方面，包括学术出版和同行评审。近年来，诸如大语言模型等人工智能工具已展现出简化众多传统上由人类编辑和评审人员处理的任务的能力。这些应用范围从自动语言和语法检查到剽窃检测、格式合规性，甚至对研究意义的初步评估。虽然人工智能极大地提高了学术流程的效率和准确性，但其整合引发了关键的伦理和方法问题，尤其是在同行评审中。人工智能缺乏人类专业知识所提供的对复杂科学内容的微妙理解，在评估研究新颖性和意义方面带来挑战。此外，存在过度依赖人工智能的风险、人工智能算法中的潜在偏差，以及与透明度、问责制和数据隐私相关的伦理问题。本综述评估了科学界对在同行评审和学术出版中整合人工智能的观点。通过探讨人工智能的潜在益处和局限性，我们旨在提供切实可行的建议，确保人工智能用作一种支持性工具，辅助而非取代人类专业知识。此类指导方针对于维护学术工作的完整性和质量至关重要，同时受益于人工智能在编辑流程中的效率提升。
