医疗教育中生成人工智能的机遇,挑战和未来方向:范围审查
Carl Preiksaitis1, Christian Rose1
1Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.
JMIR medical education
|October 20, 2023
概括
生成型人工智能 (AI) 在医学教育中提供了机遇和挑战. 需要进一步研究关键的人工智能评估技能,评估方法和人类-人工智能互动,以有效地导航其集成.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 像Chat GPT和Bard这样的生成人工智能 (AI) 工具越来越多地在各个领域使用.
- 它们在医学教育中的应用引起了极大的兴趣和关注.
- 这些人工智能技术可以产生具有各种潜在用途的新内容.
研究的目的:
- 综合医疗教育中生成人工智能的机会和局限性.
- 确定近期文献中关于生成性AI应用和挑战的普遍主题.
- 为了指导未来的研究方向在这个领域.
主要方法:
- 在 Arksey 和 O'Malley 框架下进行范围审查.
- 搜索了PubMed,科学网和谷歌学者从2022年起的英语文章.
- 包括讨论医学教育中的生成人工智能的文章,其次是数据提取和合成.
主要成果:
- 主题分析确定了诸如自主学习,模拟和写作辅助等应用.
- 重要的挑战包括学术完整性,数据准确性和潜在的学习损害.
- 拟议的研究领域:关键的人工智能评估技能,评估方法和人-人工智能互动.
结论:
- 医疗教育中生成人工智能的整合提供了机遇和挑战.
- 发展与人工智能相关的技能和能力至关重要.
- 需要深思熟虑的方法来检查在医学教育中扩大生成AI的使用.
关键词:
在这里,我们可以看到AIAIAI.巴尔德巴尔德 巴尔德巴尔德是什么意思聊天GPT 聊天 在GPT 聊天人工智能的人工智能是人工智能.教育者教育者教育者产生性的产生性.学习者学习者学习者医学教育 医学教育审查 审查 审查 审查 审查 审查探测范围 探测范围 探测范围更多相关视频
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