在医学中对人工智能的实验室课程进行结构化
1Vasile Goldiş Western University Arad, Romania.
Studies in health technology and informatics
|October 3, 2025
概括
这项研究表明,为一年级医学学生提供早期医学人工智能 (AIM) 培训是可行的. 代课程设计,专注于实际应用和快速工程,显著提高了学生的表现和项目一致性.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 课程设计 课程设计
背景情况:
- 一年级医学生往往缺乏计算机科学和数学基础知识.
- 将人工智能 (AI) 整合到医学教育中需要仔细的课程设计和教学方法.
- 之前的AI in medicine课程强调了学生在逻辑推理和项目变性方面的挑战.
研究的目的:
- 描述设计,实施和重组医学课程的AI在医学课程的第一年医学学生.
- 评估课程修改对学生学习和项目成果的影响.
- 支持在医学培训中早期整合人工智能的可行性.
主要方法:
- 两年的代课程开发包括研讨会,实践工作 (回归,神经网络) 和视频演示.
- 在第二年介绍了本体结构构建,神经网络和回归的比较分析,以及提示工程.
- 专注于应用学习活动和指导实践练习.
主要成果:
- 最初的课程突出了逻辑和本体学的困难,项目质量具有很高的变化.
- 第二年的调整,强调应用工作和快速工程,导致业绩显著提高.
- 在课程重组后,学生项目的一致性和质量得到了提高.
结论:
- 对医学学生来说,早期在医学中引入人工智能是可行的和有益的.
- 代的课程设计,专注于实际应用和对话AI技能,对于有效的AI医学教育至关重要.
- 该研究支持根据学生反和绩效数据调整教学方法的价值.
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