医学教育中的AI:医学学生的感知,课程建议和设计建议
1Antai College of economics and management, Shanghai Jiao Tong University, Shanghai, China.
BMC medical education
|November 10, 2023
概括
医学教育必须整合未来医生的人工智能 (AI) 培训. 研究生表现出更高的人工智能意识,以及诸如绩效预期和信任等因素驱动医学学生的人工智能采用.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 技术接受模型 技术接受模型
背景情况:
- 医疗人工智能正在迅速发展,需要为医疗保健专业人员更新课程.
- 目前的医学教育落后于技术整合,造成了基本的人工智能技能的差距.
- 对医学AI的系统培训对于本科和研究生医学学生来说都至关重要.
研究的目的:
- 调查影响学生接受和使用医疗AI的意图的因素.
- 确定特定的教育策略,以加强未来医疗保健专业人员的医疗AI采用.
- 将技术接受和使用统一理论 (UTAUT) 模型应用于医疗人工智能教育.
主要方法:
- 调查数据来自13所大学和33家医院的1,243名医学本科生和研究生.
- 利用技术接受和使用统一理论 (UTAUT) 模型来分析影响因素.
- 评估了医疗AI的先前经验,意识水平和使用意图.
主要成果:
- 54.3%的受访学生报告说,他们之前有过医疗AI的经验.
- 与本科生相比,医学研究生对医学AI的认识和接受程度更高.
- 与使用医疗人工智能的意图积极相关的关键因素包括绩效预期,习惯,享乐动机和信任.
结论:
- 医疗教育需要发展以整合人工智能,专注于提高学生的表现和参与度.
- 课程应该设计成用户友好和激励,以培养人工智能能力.
- 让学生具备人工智能技能对于他们在现代医学的不断变化的环境中取得成功至关重要.
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