向未来的放射学家教授人工智能:新西兰放射学课程课程的课程增强
Alan Wang1, Beau Pontre2, Sibusiso Mdletshe3
1Auckland Bioengineering Institute, The University of Auckland, New Zealand; Centre for Co-Created Ageing Research, The University of Auckland, New Zealand; Centre for Brain Research, The University of Auckland, New Zealand; Medical Imaging Research Center, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand.
Journal of medical imaging and radiation sciences
|February 6, 2026
概括
在本科放射学课程中整合人工智能 (AI) 对于发展AI素养至关重要. 这种教育视角概述了一种成功的教学AI概念的方法,为未来的放射学家准备不断发展的临床实践.
科学领域:
- 放射学 教育 放射学教育
- 医学成像技术 医学成像技术
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 正在迅速改变放射学,需要学生们对人工智能进行识字.
- 在本科放射学课程中理解AI内容集成存在差距.
- 本文讨论了在放射学课程中对结构化人工智能教育的需求.
研究的目的:
- 描述将人工智能集成到新西兰本科放射学课程中的实用方法.
- 分享作者对人工智能课程开发的思考和实施细节.
- 为其他寻求将人工智能纳入放射学教育的机构提供榜样.
主要方法:
- 整合了包括图像预处理,细分,增强,可解释AI和伦理等AI概念.
- 内容交付使用临床示例和视觉解释为非计算机科学学生.
- 该方法优先考虑了可访问性,临床相关性和与数字健康战略的协调性.
主要成果:
- 学生的初步反表明,他们对人工智能的临床应用有了更强的认识.
- 关键的实施见解包括课程排序和伦理/可解释性的价值.
- 未来的工作包括正式评估,评估整合和协作资源开发.
结论:
- 介绍了一种有效的教学方法,用于将AI整合到本科放射学课程中.
- 早期接触人工智能可以培养未来放射科医生的基本技能.
- 这一举措使放射科医生能够自信地在临床环境中利用新兴的人工智能技术.
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