生成型人工智能在护理流程表数据上表现出难以推理的困难
Courtney J Diamond1, Jennifer Thate1,2, Jennifer B Withall1
1Columbia University Department of Biomedical Informatics, New York, NY.
概括
生成型人工智能 (AI) 在解释护理文档方面表现出有限的能力,GPT-4仅正确评估了14种场景中的3种. 护士专家的表现优于人工智能,强调需要在医疗保健中为人工智能提供全面的培训数据.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 护理文档护理文档
背景情况:
- 临床医生的倦怠是一个重大问题,部分原因是过度的文档负担.
- 生成型人工智能 (AI) 提供了减少文档负担的潜在解决方案,但需要进行彻底评估.
- 评估AI在解释复杂临床数据方面的有效性对于安全实施至关重要.
研究的目的:
- 评估大型语言模型 (LLM),特别是OpenAI的GPT-4在解释护理流程表中的干预-响应关系方面的表现.
- 为了比较LLM的评估准确度与经验丰富的护士评估员的评估准确度.
- 确定用于临床文档分析的AI模型的局限性和改进领域.
主要方法:
- 使用了包含干预-响应关系作为输入数据的护理流程表.
- 采用MUC-5评估指标来评估LLM的表现.
- 将LLM (ChatGPT) 的评估与护士专家评估人员的评估进行了比较.
主要成果:
- 在14个临床场景中,ChatGPT仅对3个场景进行了正确的评估.
- 聊天GPT部分评估了14个场景中的6个,在推理中经常省略关键数据.
- 护士专家评估人员实现了100%的准确性,并提供了超出记录数据的背景.
结论:
- 像GPT-4这样的当前LLM在准确解释细微的护理文档方面存在重大局限性.
- 未来用于电子健康记录 (EHR) 的人工智能开发必须纳入各种护理叙事数据,以捕捉临床推理.
- 确保AI总结工具不会增加最终用户负担对于采用至关重要.
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