临床医生对使用人工智能对MRI脑部扫描进行分类的观点
Munaib Din1, Karan Daga2, Jihad Saoud2
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, the United Kingdom of Great Britain and Northern Ireland; Department of Radiology. Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom.
European journal of radiology
|January 13, 2025
概括
人工智能 (AI) 工具可以对放射学扫描进行分类,改善患者的途径并减少临床医生的工作量. 临床医生对人工智能辅助的MRI脑部扫描分组表现出积极支持,特别是当人工智能决策被可视化时.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 临床工作流的优化 临床工作流优化
背景情况:
- 人工智能 (AI) 提供了通过对扫描进行分类和标记关键发现来简化放射学工作流程的潜力,从而减少报告延迟和临床医生的负担.
- 将人工智能整合到临床途径中需要了解利益相关者的观点,并确定采用潜在障碍.
研究的目的:
- 调查英国临床医生关于人工智能工具对MRI脑部扫描进行分类的可接受性的观点.
- 确定AI在临床放射学途径中的整合障碍和促进因素.
主要方法:
- 一项调查分发给133名英国临床医生,评估他们对人工智能工具的看法,将MRI脑部扫描分为"正常"和"异常"类别.
- 调查参与者获得了有关AI模型培训,验证,性能和可解释性特征 (salency地图) 的信息.
主要成果:
- 71%的临床医生更喜欢人工智能辅助分组,而不是目前的MRI脑部扫描时间系统.
- 提供可解释性信息,例如可视化AI决策的热图,增加了临床医生对AI工具的信心 (60%的人更有信心).
结论:
- 临床医生对在放射学中实施人工智能辅助选工具的支持是积极的.
- 可解释性特征对于增强临床医生对AI诊断工具的信任和信心至关重要.
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