需要在药物监督和其他医疗安全关键环境中使用大型语言模型的护
Joe B Hakim1, Jeffery L Painter2, Darmendra Ramcharran3
1Harvard-MIT Department of Health Sciences and Technology, Cambridge, MA, USA.
大型语言模型 (LLM) 可以产生幻觉,产生虚假信息. 我们开发了防护,以减轻药物安全方面的这些错误,确保在关键医疗环境中可靠地使用LLM.
科学领域:
- 人工智能的人工智能
- 药物监督 药物监督 药物监督
- 医疗安全 医疗安全
背景情况:
- 大型语言模型 (LLM) 提供可扩展的知识工作能力.
- 在药物安全等高风险领域部署LLM提出了挑战,特别是"幻觉" (伪造信息).
- 药物安全背景中的不准确性可能会导致严重的患者伤害.
研究的目的:
- 开发和演示一个概念验证套件的护,以减轻LLM幻觉和药物安全方面的错误.
- 确保在医疗安全关键的环境中安全地应用LLM.
- 解决与药监相关的不正确术语的产生问题.
主要方法:
- 实施了防护,用于检测异常文件,识别不正确的药物/不良事件术语,并传达不确定性.
- 集成的护与微调的LLM用于文本到文本任务.
- 应用该方法来翻译个人病例安全报告用于药监处理.
主要成果:
- 在药监监督处理任务中证明了护的有效应用.
- 成功地减轻特定类型的幻觉和LLM产生的内容中的错误.
- 确保遵守严格的监管和质量标准.
结论:
- 开发的护框架为在高风险情况下安全部署LLM提供了工具.
- 护在各种领域广泛适用,需要医疗安全关键的环境.
- 该系统有效地消除了关键错误,提高了药监监管中的LLM可靠性.
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