一个聊天IRB? 如何应用特定的语言模型可以增强研究伦理审查审查
Sebastian Porsdam Mann1,2,3, Jiehao Joel Seah3, Stephen Latham4
1Centre for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of Copenhagen, Copenhagen, Denmark.
Journal of medical ethics
|August 5, 2025
概括
我们建议使用定制的人工智能,特别是大型语言模型 (LLM),以提高机构审查委员会 (IRB) 对人类受试者的研究监督过程的效率和一致性.
科学领域:
- 生物伦理与研究监督 生物伦理与研究监督
- 医疗保健中的人工智能
- 人类受试者研究研究对象
背景情况:
- 机构审查委员会 (IRB) 对伦理人类受试者研究至关重要.
- 目前的IRB流程面临重大挑战,包括不一致性,延误和低效率.
- 现有的审查机制很难跟上现代研究的复杂性.
研究的目的:
- 为IRB审查提出开发和实施特定应用的大型语言模型 (LLM).
- 提高道德审查过程的效率,一致性和质量.
- 探索人工智能驱动的解决方案,以改善研究监督,同时保持人类的判断力.
主要方法:
- 对IRB特定的文献和机构数据集进行微调LLM.
- 将LLM装备为最新的,与上下文相关的信息的检索能力.
- 概述潜在的应用:审查前选,初步分析,一致性检查和决策支持.
主要成果:
- 通过自动化初步任务,LLM有可能简化IRB工作流程.
- 人工智能可以帮助识别不一致性,并确保遵守道德准则.
- 拟议的系统旨在支持,而不是取代关键决策中的人类监督.
结论:
- 特定于IRB的LLM提供了一种有希望的方法来加强研究伦理监督.
- 解决有关准确性,上下文敏感性和透明度的问题对于成功实施至关重要.
- 建议进行试点研究,以评估LLM融入IRB流程的可行性和影响.
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