检查可解释的临床决策支持系统与大声思考协议
Sabrina G Anjara1, Adrianna Janik2, Amy Dunford-Stenger1
1Accenture The Dock, Dublin, Ireland.
PloS one
|September 14, 2023
概括
这项研究探讨了瘤学家如何看待人工智能解释癌症复发预测. 结果揭示了影响可解释AI (XAI) 在临床实践中的可信度和实用性的关键因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 机器学习 (ML) 提高医疗保健质量和治疗规划.
- 可解释AI (XAI) 对于理解ML模型至关重要,促进信任和采用.
- 有限的用户研究存在于黑子ML模型解释的可解释性.
研究的目的:
- 探索瘤学家对人工智能系统用于肺癌复发预测的评估.
- 通过使用专家反来完善解释模型以提高可信度和实用性.
- 了解影响临床医生对人工智能系统有用性和可靠性的看法的因素.
主要方法:
- 使用一个大声思考协议 (TAP) 与十名瘤学家.
- 采用TAP作为一种中立的方法来捕捉专家的思维过程,而没有明确的提示.
- 进行了口头回应的主题分析,以确定关键主题.
主要成果:
- 确定了影响瘤学家对人工智能系统感知的五个关键主题.
- 引起影响人工智能解释可信度和有用性的因素.
- 获得了关于将可解释的人工智能集成到日常临床工作流程中的见解.
结论:
- 这项研究更深入地了解了瘤学家如何评估可解释的AI工具.
- 研究结果可以为临床环境开发更可信和更有用的AI解释模型提供信息.
- 强调了以用户为中心的设计和评估在医疗保健专业人员的AI实施中的重要性.
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