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相关实验视频

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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人类专业知识在人工智能协作同行评审过程中

Janine Overcash1

  • 1Janine Overcash.

Oncology nursing forum
|August 24, 2025
PubMed
概括

不公开的人工智能 (AI) 产生的同行评价引发了重大的伦理问题. 随着人工智能在学术界的使用越来越多,它在研究完整性方面的作用迫切需要关注.

科学领域:

  • 人工智能
  • 学术诚信
  • 科学出版

背景情况:

  • 人工智能 (AI) 工具在学术界日益普及.
  • 对学生在学术任务中未公开使用人工智能的担忧越来越大.
  • 研究人员在学术交流中采用类似的人工智能的潜力.

研究的目的:

  • 探索未公开的人工智能产生的同行评价的伦理含义.
  • 评估人工智能对同行评审过程的完整性的潜在影响.
  • 在科学研究中强调人工智能使用的透明度.

主要方法:

  • 对文本生成当前人工智能能力的定性分析.
  • 对研究中人工智能的现有伦理准则的审查.
  • 在同行评审中讨论人工智能使用的潜在场景.

主要成果:

  • 人工智能可以产生可信的同行评审评论,引发对真实性的担忧.
  • 缺乏公开性加剧了伦理问题,可能会破坏科学话语.
  • 人工智能易于采用,这表明研究人员广泛使用是合理的.

结论:

关键词:
人工智能伦理学同行评审出版业进行研究

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  • 未公开的AI产生的同行评价对研究完整性构成严重威胁.
  • 需要明确的指导方针和透明的做法来解决同行评审中的人工智能问题.
  • 积极的措施是保持对科学出版过程的信任至关重要的.