大型语言模型和医疗教育:为快速变革的准备 实习生将如何学习成为医生
Akshay Ravi1, Aaron Neinstein1,2, Sara G Murray1,3
1Department of Medicine.
ATS scholar
|October 5, 2023
概括
大型语言模型 (LLM) 通过减少行政负担和加强患者教育,为医疗保健提供了变革性的潜力. 他们的融入医学教育对于培养未来临床医生的临床推理和基于证据的实践至关重要.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 人工智能在医学中的应用
- 医疗教育 医学教育
背景情况:
- 人工智能 (AI) 对改变医疗保健具有重大前景,但广泛实施仍然有限.
- 当前的人工智能应用往往专注于可预测的,而不是可操作的医疗保健问题.
- 大型语言模型 (LLM) 是一个重要的进步,为临床医生提供可访问性和快速测试.
研究的目的:
- 探索LLM在医疗保健和医学教育中的潜力.
- 确定与LLM整合到临床实践和培训中的机会和挑战.
- 强调需要开发支持临床推理,基于证据的医学和基于案例的培训的LLM.
主要方法:
- 分析目前的趋势和LLMs在医疗保健中的潜在应用.
- 讨论LLM对医学教育的影响,包括临床文档和实习生技能发展.
- 探索实习生在开发和完善未来的LLM应用程序中的协作作用.
主要成果:
- 法律法学士有潜力显著减少医疗保健专业人员的文书工作负担.
- 通过弥合信息差距,LLM可以改善患者教育.
- 法律学士准备改变医学课程,强调临床推理和基于证据的实践.
结论:
- LLM正在迅速融入临床实践和医学教育中.
- 未来的LLM发展必须优先支持实习生和教育工作者.
- 开发人员,教育工作者和实习生之间的协作努力对于LLM在医疗保健中的负责任整合至关重要.
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