Leveraging large language models to foster equity in healthcare.
Leveraging large language models to foster equity in healthcare.
复制标题
利用大型语言模型促进医疗保健公平。
DOI：
10.1093/jamia/ocae055
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Bates,DavidW
中科院分区：
文献类型：
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作者：
Rodriguez,JorgeA;Alsentzer,Emily;Bates,DavidW
大型语言模型（llm）有望改变医疗服务，但其对卫生公平的影响尚不清楚。虽然边缘化人群历来被排除在早期技术开发之外，但法学硕士提供了一个改变我们开发、评估和实施新技术的方法的机会。从这个角度来看，我们描述了法学硕士在支持卫生公平方面的作用。材料和方法我们应用国家少数民族健康和健康差异研究所（NIMHD）的研究框架来探索法学硕士在健康公平方面的应用。结果：我们为法学硕士如何改善个人、家庭和组织、社区和人口健康的健康公平提供了机会。我们描述了新出现的问题，包括有偏见的数据，有限的技术扩散和隐私。最后，我们强调了关于快速工程、检索增强、数字包容、透明度和减少偏见的建议。结论法学硕士支持卫生公平的潜力取决于从一开始就将卫生公平作为重点。
ObjectivesLarge language models (LLMs) are poised to change care delivery, but their impact on health equity is unclear. While marginalized populations have been historically excluded from early technology developments, LLMs present an opportunity to change our approach to developing, evaluating, and implementing new technologies. In this perspective, we describe the role of LLMs in supporting health equity.Materials and MethodsWe apply the National Institute on Minority Health and Health Disparities (NIMHD) research framework to explore the use of LLMs for health equity.ResultsWe present opportunities for how LLMs can improve health equity across individual, family and organizational, community, and population health. We describe emerging concerns including biased data, limited technology diffusion, and privacy. Finally, we highlight recommendations focused on prompt engineering, retrieval augmentation, digital inclusion, transparency, and bias mitigation.ConclusionThe potential of LLMs to support health equity depends on making health equity a focus from the start.