来自国际卫生信息教育伙伴关系 (IPHIE) 的学生观点2024年大师班
Evan Christensen1, Courtney Gauchel1, Lourdes Valdez1
1University of Utah, Department of Biomedical Informatics; Salt Lake City, Utah, USA.
研究生通过全球大师班探索医疗保健中的大型语言模型 (LLM). 该项目强调了国际合作对于发展医疗信息学技术和软技能的重要性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 国际健康信息学教育伙伴关系 (IPHIE) 促进全球在健康信息学方面的合作.
- 2024年IPHIE大师班召集了研究生和教师,讨论医疗保健中的大型语言模型 (LLM).
研究的目的:
- 检查LLM在医疗保健环境中的使用,感知和影响.
- 评估健康信息学国际合作的教育价值.
主要方法:
- 学生们共同设计,试点并分析了一项关于医疗保健提供者和患者对LLM利用情况的调查.
- 一个大师课格式促进了国际交流和技能发展.
主要成果:
- 该调查探讨了当前的LLM应用和医疗保健中的用户观点.
- 尽管存在物流方面的挑战,但学生们提高了领导力,解决问题的能力和沟通能力.
- 国际合作经验对技能获取非常有价值.
结论:
- 全球合作对于推进健康信息学教育和实践至关重要.
- 发展技术专长和软技能,为多学科医疗保健团队的专业人员做好准备.
- 对LLM在医疗保健中的整合进行进一步研究是有必要的.
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