一个框架,使LLMs进入透明度和可信度的监管环境,并将其应用于药物标签文件
Leihong Wu1, Joshua Xu1, Shraddha Thakkar2
1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Rd, Jefferson AR, 72211, USA.
Regulatory toxicology and pharmacology : RTP
|April 3, 2024
概括
一个新的框架,askFDALabel,使用安全的大型语言模型 (LLMs) 进行高效的FDA药物标签审查. 这种计算成本低廉的解决方案与监管应用程序的ChatGPT性能相匹配.
科学领域:
- 计算语言学 计算语言学
- 监管科学 监管科学
- 医疗保健中的人工智能
背景情况:
- 监管机构面临的挑战是广泛的文档审查.
- 大型语言模型 (LLM) 具有潜力,但在专有数据,定制和透明度方面存在局限性.
- 对于监管任务,需要一个安全,本地化和定制化的LLM解决方案.
研究的目的:
- 为了开发一个安全的框架,请问FDALabel,审查FDA药物标签文件.
- 解决与在监管环境中使用一般的LLM相关的挑战.
- 为高效和透明的监管研究创建一个定制的解决方案.
主要方法:
- 制定了在安全的IT环境中运行的askFDALabel框架.
- 实现了模块S,使用词嵌入用于标签文档中的语义搜索.
- 开发了模块T与调整的LLM进行问答和文本生成,引用模块S的输出.
主要成果:
- askFDALabel框架使小LLM能够实现与ChatGPT相当的性能.
- 对于监管应用,该解决方案在计算上是廉价的.
- 展示了一个安全的途径,以利用LLM在机构运营中的定制功能.
结论:
- AskFDALabel为监管文件审查提供安全和定制的LLM解决方案.
- 该框架提高了FDA药物标签流程的效率和透明度.
- 这种方法通过利用LLM来满足专门的监管研究需求来支持机构的运作.
关键词:
人工智能的人工智能是人工智能.药物标签上的药物标签.在FDALabel.大型语言模型 (LLM)监管科学是一种监管科学.检索增强生成 (RAG) 是一个.透明度 透明度 透明度他们是值得信赖的,值得信赖的,值得信赖的.更多相关视频
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