儿科教育中的大型语言模型:当前的用途和未来的潜力
Srinivasan Suresh1,2, Sanghamitra M Misra3,4
1Divisions of Health Informatics & Emergency Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Pediatrics
|August 7, 2024
概括
生成型人工智能,特别是大型语言模型 (LLM),可以通过帮助课程,培训和患者材料来改变儿科教育. 仔细的专家审查对于管理不准确性和安全实施的伦理问题等风险至关重要.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 儿科 儿科 儿科
背景情况:
- 生成型人工智能 (AI),特别是大型语言模型 (LLM),在儿科教育中带来了重大机遇和挑战.
- 法律学士可以协助课程开发,个性化实习生支持,并加强儿科医生的临床实践.
研究的目的:
- 探索儿童教育中生成人工智能的历史,当前应用和挑战.
- 提供LLM能力的例子,并讨论未来负责整合的方向.
主要方法:
- 审查当前的文献和在医学教育LLMs的应用,特别是儿科.
- 分析与LLM使用相关的潜在好处,风险和伦理考虑.
主要成果:
- 法律学士可以增强课程设计,创建个性化的学习计划,改善信息检索,并完善患者教育材料.
- 当前的LLM可能会产生不准确性 ("幻觉"),并引发关于偏见和抄袭的伦理问题.
结论:
- 内容专家明智地使用LLM可以利用其在儿科教育中的好处,同时减轻风险.
- 制定明确的指导方针和政策对于在儿童医疗保健教育中安全有效地采用人工智能至关重要.
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