放射学AI培训和评估-挑战,创新,以及前进的道路
Girija Agarwal1, Kavish Maroo2, Paymon Zomorodian3
1Department of Clinical Radiology, St Mary's Hospital, Imperial College Healthcare Trust, London W2 1NY, United Kingdom.
BJR open
|December 15, 2025
概括
人工智能 (AI) 正在彻底改变放射学,但培训计划缺乏一致的AI教育和评估. 未来的放射科医生需要结构化的AI素养,以有效地将这些工具集成到临床实践中.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 医疗教育 技术 技术 医学教育
背景情况:
- 人工智能 (AI) 在医疗设备中越来越普遍,特别是在成像中.
- 放射学培训计划在调整课程以包括人工智能能力方面面临挑战.
- 目前的医学课程显示AI的整合不一致,缺乏能力评估.
研究的目的:
- 审查英国放射学培训课程中人工智能的现状.
- 探索人工智能对能力评估和专业评估的影响.
- 检查人工智能驱动的教育资源,以加强放射学培训.
主要方法:
- 在英国医学培训课程中对AI的文献综述,重点是放射学.
- 对AI对FRCR,ARCP和应急评估等评估的潜在影响的分析.
- 探索人工智能辅助的培训平台和教育资源.
主要成果:
- 人工智能采用正在加速,大多数人工智能医疗设备与成像有关.
- 人工智能融入放射学课程和评估是不一致的.
- 由人工智能驱动的教育工具为增强学习体验提供了潜力.
结论:
- 放射学培训必须不断发展,包括人工智能识字和能力评估.
- 提出了结构化的国家人工智能教学和以人工智能为重点的评估.
- 为放射科医生提供人工智能技能对于未来的医疗保健转型至关重要.
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