通过人工智能增强辐射瘤学教育:应用,局限性和未来方向的审查
Zhi Xuan Ng1, Ivy Weishan Ng2, Teng Hwee Tan2
1National University Cancer Institute, Singapore (NCIS), NUH Medical Centre (NUHMC), 5 Lower Kent Ridge Road Levels 8-10, Singapore, 119074, Singapore. ng_zhixuan@hotmail.com.
概括
人工智能 (AI) 正在改变辐射瘤学教育. 将人工智能工具集成到居住计划中可以增强培训,但需要关键评估技能和教师支持才能有效实施.
科学领域:
- 医学物理 医学物理
- 在瘤学瘤学.
- 医学教育 医学教育
背景情况:
- 人工智能 (AI) 正在辐射瘤学领域迅速发展,影响到自动轮和治疗规划等领域.
- 放射性瘤实习生培训落后于这些技术进步,为未来的从业者创造了教育差距.
研究的目的:
- 审查当前在辐射瘤学中的AI应用.
- 评估人工智能工具如何改善临床,程序和研究领域的居民教育.
主要方法:
- 使用主要数据库 (MEDLINE,EMBASE,CENTRAL,CINAHL) 进行了叙事文献审查.
- 关键词包括"人工智能"",医学教育"",辐射瘤学"和"自动轮".
- 专家评论和关于教育环境中人工智能的研究被纳入.
主要成果:
- 由人工智能驱动的工具,如自动细分反,计划优化模拟器和决策支持引擎,可以增强学习.
- 人工智能提供了诸如改善对复杂案件的访问,实时反和减少对教师的依赖等好处.
- 潜在的风险包括过度依赖,算法偏见和对AI输出的误解.
结论:
- 人工智能具有显著的潜力,可以彻底改变放射瘤学居民教育.
- 结构化的课程整合和教师发展对于成功实施人工智能至关重要.
- 居民需要培训,以批判性地评估人工智能工具,并保持以患者为中心的决策.
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