什么使人工智能做出"好"的决定? 在儿科护理中的一个有基础的理论研究
Melissa D McCradden1,2,3, Kelly Thai2, Azadeh Assadi4,5
1The Hospital for Sick Children, Toronto, Ontario, Canada melissa.mccradden@adelaide.edu.au.
BMJ evidence-based medicine
|February 12, 2025
概括
使用机器学习 (ML) 模型开发临床决策框架需要考虑证据,患者因素和模型性能. 这项研究提供了一个可行的指南,以支持临床医生有效地使用AI工具.
科学领域:
- 临床决策 - 临床决策
- 医疗保健中的机器学习
- 人工智能在医学中的应用
背景情况:
- 临床决策越来越多地涉及患者级数据和先进分析.
- 将机器学习 (ML) 模型集成到临床实践中需要一个结构化的方法,以确保安全有效的使用.
- 现有的框架可能无法完全解决ML驱动的患者护理的复杂性.
研究的目的:
- 在使用机器学习 (ML) 模型进行干预,患者层面的决策时,为良好的临床决策制定一个全面的框架.
- 为临床医生提供可操作的指南,以导航ML洞察力的整合到患者护理中.
- 确保在医疗保健中使用人工智能时保持道德和医疗法律标准.
主要方法:
- 基于理论的定性访谈研究,对16名医疗保健专业人员和一名ML专家进行了采访.
- 半结构面试模拟了重症监护病房的交付与假设的患者病例和ML模型可视化.
- 框架开发涉及成员检查和患者咨询,以改进.
主要成果:
- 基于医学法律和伦理标准,开发了一个框架,将ML模型推断纳入临床决策.
- 关键的决策因素包括患者证据,知识的适用性,患者/家庭/当地环境,模型细节和模型对患者数据的表示.
- 参与者表现出自动化偏见,其中一些人对ML预测有偏见,另一些人优先考虑他们的医学知识;人们注意到对本地性能数据的持续渴望.
结论:
- 使用人工智能工具进行有效的临床决策需要多领域的反思,平衡ML见解与临床专业知识.
- 提供了一个可操作的框架和问题指南,以支持临床医生使用人工智能做出合理的决定.
- 这项工作促进了ML在患者级临床实践中的负责任和道德整合.
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