使用大型语言模型生成的临床病例的可行性和教育价值
Anna Berbenyuk1, Leigh Powell1, Nabil Zary1
1Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health, Dubai, UAE.
Studies in health technology and informatics
|August 23, 2024
概括
生成型人工智能 (AI) 可以创建现实的医学教育案例,增强医疗保健专业人员的基于案例的学习 (CBL). 人工智能产生的案例显示出在继续医学教育中提高参与度和批判性思维的潜力.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 基于案例的学习 (CBL) 对于在医疗保健中应用理论知识至关重要.
- 创建有效的临床病例需要大量的时间和专业知识.
- 生成型人工智能 (AI) 为高效的案例生成提供了一个潜在的解决方案.
研究的目的:
- 探索AI产生的病例在持续医学教育中的可行性和教育价值.
- 为MENA地区评估人工智能生成的COVID-19场景.
- 评估AI在增强基于案例的学习方面的潜力.
主要方法:
- 利用生成AI分析医疗数据并创建临床场景.
- 专注于与MENA地区相关的COVID-19病例情景.
- 评估AI产生的案例的教育价值和可行性.
主要成果:
- 人工智能产生的案例显示了促进学习者参与的潜力.
- 人工智能产生的案例被发现可以促进批判性思维技能.
- 该研究表明,人工智能产生的案例适合各种教育水平.
结论:
- 生成性人工智能是创建多样化和最新的临床病例的可行工具.
- 将人工智能整合到基于案例的学习中可以增强医学教育.
- 需要进一步的研究来克服医疗教育中人工智能的采用挑战.
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