大型语言模型和全球卫生公平:在LMIC中公平采用路线图
Haichao Chen1,2, Dian Zeng1, Yiming Qin1
1Tsinghua Medicine, Tsinghua University, Beijing, China.
The Lancet regional health. Western Pacific
|October 29, 2025
概括
大型语言模型 (LLM) 可以改善全球卫生公平,但发展集中在高收入国家. 最近的LLM提供了更低的成本和开源选项,显示了全球利益的前景.
科学领域:
- 人工智能的人工智能
- 全球健康 全球健康
- 健康 公平 卫生 公平
背景情况:
- 大型语言模型 (LLM) 具有解决全球健康不平等问题的潜力,特别是在低收入和中等收入国家 (LMICs).
- 目前的LLM开发和部署集中在高收入国家 (HIC),可能会增加健康差距.
- 最近的进展,如成本降低和开源框架,为全球重新平衡LLM的好处提供了机会.
研究的目的:
- 检查全球各地区LLM部署的不平衡.
- 在LMICs中确定LLM采用的关键障碍.
- 为公平地采用LLM提出路线图,以改善全球健康.
主要方法:
- 对当前LLM部署和挑战的观点分析.
- 评估LLM的进步和全球健康公平的机会.
- 开发一个五维路线图 (人,产品,平台,流程,政策).
主要成果:
- 在LLM的开发和部署中存在显著的不平衡,有利于HIC.
- 在LMIC中采用LLM的障碍包括成本,基础设施和数据可访问性.
- 新的LLM功能有潜力弥合差距并促进健康公平.
结论:
- 公平的LLM采用需要解决部署失衡和LMIC特定障碍.
- 战略性,多维的方法对于利用LLM来推进全球健康公平至关重要.
- 拟议的路线图为通过LLM技术在全球卫生领域实现包容性进步提供了框架.
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