利用大型语言模型促进医疗保健公平。
Leveraging large language models to foster equity in healthcare.
机构信息
Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, United States.
Harvard Medical School, Boston, MA 02115, United States.
出版信息
J Am Med Inform Assoc. 2024 Sep 1;31(9):2147-2150. doi: 10.1093/jamia/ocae055.
OBJECTIVES
Large 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 METHODS
We apply the National Institute on Minority Health and Health Disparities (NIMHD) research framework to explore the use of LLMs for health equity.
RESULTS
We 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.
CONCLUSION
The potential of LLMs to support health equity depends on making health equity a focus from the start.
目的
大型语言模型（LLM）有望改变医疗服务模式，但它们对健康公平的影响尚不清楚。虽然边缘化群体在历史上被排除在早期技术发展之外，但 LLM 为我们改变开发、评估和实施新技术的方法提供了机会。在这篇观点文章中，我们描述了 LLM 在支持健康公平方面的作用。
材料与方法
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 LLM 在健康公平方面的应用。
结果
我们提出了 LLM 如何在个人、家庭和组织、社区和人口健康方面改善健康公平的机会。我们描述了一些新出现的问题，包括数据偏差、技术扩散有限和隐私问题。最后，我们强调了一些建议，重点是提示工程、检索增强、数字包容、透明度和偏差缓解。
结论
LLM 支持健康公平的潜力取决于从一开始就将健康公平作为重点。
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