放射学人工智能的教育能力:一个范围审查
Sunam Jassar1, Zili Zhou2, Sierra Leonard2
1Department of Medical Imaging, University of Toronto, Toronto, Canada (S.J., L.P.).
Academic radiology
|July 22, 2025
概括
人工智能 (AI) 在放射学中的整合需要新的放射科医生能力. 这项研究确定了安全有效使用人工智能的关键知识,技能和态度,突出了在放射学实习计划中需要标准化人工智能教育的需要.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学教育 放射学教育
背景情况:
- 人工智能 (AI) 越来越多地融入到放射学实践中.
- 放射科医生在人工智能方面的基本能力缺乏明确性.
- 放射学实习计划需要对AI相关技能发展的指导.
研究的目的:
- 用人工智能识别放射科医生的关键知识,技能和态度.
- 在放射学教育中为人工智能能力的发展提供信息.
主要方法:
- 使用Arksey和O'Malley的方法进行了范围审查.
- 在主要的电子数据库 (PubMed,Embase,Scopus,ERIC) 搜索了2010-2024年期间的文章.
- 两位独立审查员选了5920篇文章,其中49篇符合数据提取的纳入标准.
主要成果:
- 在AI模型开发,评估,临床实施,偏见,伦理和监管方面确定了核心能力.
- 发现了对能力的多样化观点,重点关注技术AI开发或临床应用.
- 突出了目前为放射科医生的人工智能教育计划中的异质性.
结论:
- 目前缺乏关于放射科医生的基本人工智能知识,技能和态度的共识.
- 在放射学实习计划中的AI教育内容中存在实质性的异质性.
- 需要进一步的研究来建立核心能力,并将人工智能培训整合到放射学实习计划中.
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