开发一种初步的患者安全分类系统,用于生成AI
Bat-Zion Hose1,2, Jessica L Handley3, Joshua Biro3
1National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA bat-zion.hose@medstar.net.
BMJ quality & safety
|January 3, 2025
概括
一个新的分类系统有助于对医疗保健中生成人工智能 (AI) 的患者安全风险进行分类. 该系统有助于监控来自人工智能工具的错误,例如大型语言模型 (LLM) 和环境数字书记.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 人工智能的人工智能
- 患者安全 患者安全
背景情况:
- 生成型人工智能 (AI) 在医疗保健领域提供了变革性的潜力.
- 与人工智能相关的患者安全风险的有效分类和监测至关重要.
- 现有的医疗保健人工智能安全框架有限.
研究的目的:
- 开发和评估用于生成AI患者安全错误的初步分类系统.
- 评估系统在对各种AI医疗保健应用中的错误进行分类方面的实用性.
- 建立在临床环境中强大的AI错误监测的基础.
主要方法:
- 设计了一种新的分类系统,围绕AI输入和输出阶段以及特定的错误类型学进行结构化.
- 该系统应用于两个不同的生成AI应用程序:面向患者的大型语言模型 (LLM) 和环境数字书记 (ADS) 系统.
- 错误分类侧重于类型,频率和临床意义.
主要成果:
- 在LLM分析 (27个查询) 中,发现了45个错误,其中遗漏是最常见的 (42%);25%的错误具有高临床意义.
- 在ADS模拟 (11次访问) 中,发现了66个错误,其中遗漏占主导地位 (83%);没有发现具有高度临床意义的错误.
- 该分类系统有效地对LLM和ADS应用程序中的输出错误进行了分类.
结论:
- 开发的分类系统证明了在识别和分类医疗保健中生成AI患者安全错误时的实用性.
- 在分析的两种AI应用中,遗漏错误普遍存在,突出了需要改进的关键领域.
- 该系统为提高AI在临床实践中的安全性和可靠性提供了基础框架.
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