在放射学教育中适当依赖人工智能
Matthew D Li1, Brent P Little2
1Department of Radiology and Diagnostic Imaging, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Journal of the American College of Radiology : JACR
|July 1, 2023
概括
放射科医生可能会过度依赖人工智能 (AI) 工具,影响团队表现. 放射学教育必须适应,教学学员明智地使用人工智能和安全实践的基础技能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学教育 放射学教育
背景情况:
- 人工智能 (AI) 越来越多地用于医学诊断.
- 过度依赖人工智能可以降低人类在决策任务中的表现.
- 人工智能在放射学中的整合需要新的培训模式.
研究的目的:
- 检查人工智能过度依赖放射学学员的发展情况.
- 探索人工智能过度依赖放射学的缓解策略.
- 提出一个框架,在放射学培训中适当地依赖人工智能.
主要方法:
- 对人类-人工智能交互研究的审查.
- 对放射学实习生潜在的过度依赖情景的分析.
- 开发一个人工智能增强教育框架.
主要成果:
- 过度依赖人工智能可能会阻碍学员发展基本的感知和基于知识的技能.
- 人工智能增强教育可以成为减轻过度依赖的工具.
- 实习生需要基础放射学专业知识,以安全有效地使用AI工具.
结论:
- 放射学教育必须不断发展,以应对人工智能集成的挑战.
- 培训应侧重于培养关键评估技能,并与人工智能工具熟练度一起.
- 对人工智能依赖的平衡方法对于保持诊断质量和安全至关重要.
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