在医学教育中生成语言模型的出现
Mert Karabacak1, Burak Berksu Ozkara2, Konstantinos Margetis1
1Department of Neurosurgery, Mount Sinai Health System, New York, NY, United States.
JMIR medical education
|June 6, 2023
概括
人工智能 (AI) 和生成语言模型 (GLM) 为医学教育提供了强大的工具,通过模拟和个性化的反来增强学习. 解决AI内容质量,偏见和道德问题对于负责任的整合至关重要.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 数字学习环境 数字学习环境
背景情况:
- 人工智能 (AI) 和生成语言模型 (GLM) 越来越多地被认为具有改变医学教育的潜力.
- 当前的教育方法可以从人工智能驱动的创新中受益,例如现实的模拟和个性化的反.
研究的目的:
- 探索将AI和GLM整合到医学教育中的机遇和挑战.
- 确定确保AI在医学培训中的质量,准确性和道德使用的策略.
主要方法:
- 文献综述和对医疗教育中人工智能应用的当前研究的综合.
- 分析潜在的好处,包括增强的模拟,数字患者和个性化的反.
- 识别诸如内容质量,偏见和道德/法律考虑等挑战.
主要成果:
- 人工智能和GLM可以提供现实的模拟,数字患者,个性化的反,并克服语言障碍.
- 关键的挑战包括确保人工智能产生的内容准确性,减轻偏见,并解决伦理和法律问题.
- 制定准则,透明的人工智能模型和跨学科合作对于负责任的实施至关重要.
结论:
- 人工智能和GLM对改善医学学生的成绩和学习经验具有重大承诺.
- 减轻风险需要严格的评估,偏见评估,以及建立明确的伦理和政策框架.
- 持续的研究和合作对于在医学教育中有效和负责任地采用人工智能至关重要,最终可以改善患者护理.
关键词:
人工智能驱动的反是人工智能驱动的反.聊天GPT 聊天 在GPT 聊天学术诚信 学术诚信人工智能的人工智能是人工智能.评价 评价 评价 评价生成性语言模型的模型.学习环境学习环境医学教育 医学教育医学院的学生医学院的学生刺激是一种刺激.技术技术的技术技术的技术.更多相关视频
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