可解释的人工智能决策支持在远程医疗中提高了喉查的准确性
Catalina Gomez1, Brittany-Lee Smith2, Alisa Zayas2
1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Communications medicine
|July 24, 2024
概括
人工智能临床决策支持系统 (CDSS) 在远程医疗中提高了链杆菌喉诊断的准确性. 然而,临床医生对人工智能的信心较低,需要加强人机协作,以便有效地采用虚拟医疗保健.
科学领域:
- 远程医疗和数字健康创新.
- 临床决策支持系统 (CDSS) 和人工智能 (AI).
- 传染病的诊断和管理.
背景情况:
- 智能手机获取的图像为远程诊断在远程医疗提供了新的途径.
- 在采用基于AI的临床决策支持系统 (CDSS) 时,临床医生的信任和理解至关重要.
- 这项研究调查了人工智能CDSS用于智能手机图像中检测链球菌喉炎 (链球菌喉炎) 的人类-AI交互范式.
研究的目的:
- 检查不同的人类-人工智能交互范式如何影响临床医生对AI CDSS对链球菌喉检测的反应.
- 评估可解释AI对临床医生的准确性,确认性测试,信任和理解的影响.
主要方法:
- 通过在线调查,对121名远程医疗服务提供商进行了一项随机实验.
- 参与者通过修改的中心分数或可解释的AI原型来评估临床细节.
- 线性模型分析了可解释的人工智能对诊断准确度,确认性测试,信任和理解的影响.
主要成果:
- 与Centor得分相比,基于AI的CDSS改善了临床医生的预测.
- 尽管与AI预测的一致性更高,但临床医生报告对AI的信任程度低于Centor得分.
- 对人工智能的信任度下降导致了对面确认测试的要求增加.
结论:
- 基于人工智能的CDSS可以提高远程医疗中远程链状喉查的准确性.
- 改善人机协作,特别是信任和可理解性,对于人工智能集成至关重要.
- 有效的AI整合确保了智能手机和AI在虚拟医疗保健中的最佳利用,并防止了抗生素过度处方.
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