用定制大模型提升科研伦理审查效率与一致性
Development of Application-Specific Large Language Models to Facilitate Research Ethics Review
- 为伦理委员会定制大模型，基于机构文献和数据微调
- 可实现预审筛查、一致性检查等四类辅助功能
- 适合需高效审查的高校/医院科研团队使用
机构审查委员会（IRB）在保障人类受试者研究伦理方面至关重要，但面临标准不一、耗时长、效率低等问题。本文提出开发面向特定应用的大语言模型（LLM），用于辅助IRB审查流程。这些专用于IRB的LLM将基于机构特有文献和数据集进行微调，并具备检索能力，可获取最新、相关的上下文信息。潜在应用场景包括预审筛查、初步分析、一致性检查和决策支持。尽管存在准确性、上下文敏感性及人工监督等挑战，仍需警惕对AI的过度依赖和透明度不足问题。通过提升审查效率与质量，同时保留关键决策中的人类判断，此类定制化大模型有望成为改善科研监管的有力工具。作者呼吁开展试点研究，评估该方法的可行性与实际影响。
原文摘要 · Abstract (English)
Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach.
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