探索课程考虑因素,为未来的放射科医生准备人工智能辅助的医疗保健环境:范围审查协议
Chamandra Kammies1,2, Elize Archer1, Penelope Engel-Hills3
1Department of Health Professions Education, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
本范围审查审查对人工智能 (AI) 整合所需的放射学课程调整进行了审查. 调查结果将指导教育变革,以确保放射科医生为人工智能辅助医疗服务做好准备.
科学领域:
- 放射学 放射学 放射学 放射学
- 医疗成像医学成像
- 健康 职业 教育 教育 专业
背景情况:
- 人工智能 (AI) 越来越多地用于放射学实践.
- 随着人工智能集成,X光学家的角色在不断发展.
- 为了在放射学中采用人工智能,需要进行教育修改.
研究的目的:
- 探索和综合有关人工智能的放射学课程调整的文献.
- 准备放射学学生用于人工智能辅助的医疗保健环境.
主要方法:
- 使用乔安娜·布里格斯研究所的方法进行范围审查.
- 在Scopus,EBSCOhost,PubMed和科学网络中进行的搜索.
- 由两名审查员进行独立审查,第三名审查员解决冲突.
主要成果:
- 最初的搜索结果为2224条记录;1930篇文章正在进行标题/摘要选.
- PRISMA-ScR检查清单将指导结果报告.
- 数据分析将以表格和叙事形式呈现.
结论:
- 将课程调整的证据映射到地图上对于将AI整合到放射学中至关重要.
- 适应教育确保了有能力和安全的放射科医生实践与AI.
- 调查结果将为未来的放射科医生提供课程开发信息.
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