Abstract：
OBJECTIVE: Artificial intelligence (AI) is reshaping health and medicine, especially through its potential to address health disparities in low- and middle-income countries (LMICs). However, there are several issues associated with the use of AI that may reduce its impact and potentially exacerbate global health disparities. This study presents the key issues in AI deployment faced by LMICs.
METHODS: Thematic analysis.
METHODS: PubMed, Scopus, Embase and the Web of Science databases were searched, from the date of their inception until September 2023, using the terms \"artificial intelligence\", \"LMICs\", \"ethic∗\" and \"global health\". Additional searches were conducted by snowballing references before and after the primary search. The final studies were chosen based on their relevance to the topic of this article.
RESULTS: After reviewing 378 articles, 14 studies were included in the final analysis. A concept named the \'AI Deployment Paradox\' was introduced to focus on the challenges of using AI to address health disparities in LMICs, and the following three categories were identified: (1) data poverty and contextual shifts; (2) cost-effectiveness and health equity; and (3) new technological colonisation and potential exploitation.
CONCLUSIONS: The relationship between global health, AI and ethical considerations is an area that requires systematic investigation. Relying on health data inherent with structural biases and deploying AI without systematic ethical considerations may exacerbate global health inequalities. Addressing these challenges requires nuanced socio-political comprehension, localised stakeholder engagement, and well-considered ethical and regulatory frameworks.
METHODS: Thematic analysis.
METHODS: PubMed, Scopus, Embase and the Web of Science databases were searched, from the date of their inception until September 2023, using the terms \"artificial intelligence\", \"LMICs\", \"ethic∗\" and \"global health\". Additional searches were conducted by snowballing references before and after the primary search. The final studies were chosen based on their relevance to the topic of this article.
RESULTS: After reviewing 378 articles, 14 studies were included in the final analysis. A concept named the \'AI Deployment Paradox\' was introduced to focus on the challenges of using AI to address health disparities in LMICs, and the following three categories were identified: (1) data poverty and contextual shifts; (2) cost-effectiveness and health equity; and (3) new technological colonisation and potential exploitation.
CONCLUSIONS: The relationship between global health, AI and ethical considerations is an area that requires systematic investigation. Relying on health data inherent with structural biases and deploying AI without systematic ethical considerations may exacerbate global health inequalities. Addressing these challenges requires nuanced socio-political comprehension, localised stakeholder engagement, and well-considered ethical and regulatory frameworks.
摘要：
目标：人工智能（AI）正在重塑健康和医学，特别是通过其解决低收入和中等收入国家（LMICs）健康差距的潜力。然而,与使用AI相关的几个问题可能会减少其影响，并可能加剧全球健康差异。本研究提出了LMIC在AI部署中面临的关键问题。
方法：主题分析。
方法：PubMed，Scopus,搜索了Embase和WebofScience数据库，从成立之日起至2023年9月，使用术语“人工智能”，\"LMIC\",“道德*”和“全球健康”。在主要搜索之前和之后，通过滚雪球参考进行了其他搜索。最终的研究是根据它们与本文主题的相关性选择的。
结果：在回顾了378篇文章之后，14项研究纳入最终分析。引入了一个名为“AI部署悖论”的概念，以关注使用AI解决LMIC健康差距的挑战。并确定了以下三个类别：（1）数据贫困和背景变化；（2）成本效益和健康公平性；（3）新技术殖民和潜在开发。
结论：全球健康之间的关系，人工智能和道德考虑是一个需要系统调查的领域。依赖具有结构性偏见的健康数据，以及在没有系统伦理考虑的情况下部署人工智能，可能会加剧全球健康不平等。应对这些挑战需要细致入微的社会政治理解，本地化的利益相关者参与，以及深思熟虑的道德和监管框架。
方法：主题分析。
方法：PubMed，Scopus,搜索了Embase和WebofScience数据库，从成立之日起至2023年9月，使用术语“人工智能”，\"LMIC\",“道德*”和“全球健康”。在主要搜索之前和之后，通过滚雪球参考进行了其他搜索。最终的研究是根据它们与本文主题的相关性选择的。
结果：在回顾了378篇文章之后，14项研究纳入最终分析。引入了一个名为“AI部署悖论”的概念，以关注使用AI解决LMIC健康差距的挑战。并确定了以下三个类别：（1）数据贫困和背景变化；（2）成本效益和健康公平性；（3）新技术殖民和潜在开发。
结论：全球健康之间的关系，人工智能和道德考虑是一个需要系统调查的领域。依赖具有结构性偏见的健康数据，以及在没有系统伦理考虑的情况下部署人工智能，可能会加剧全球健康不平等。应对这些挑战需要细致入微的社会政治理解，本地化的利益相关者参与，以及深思熟虑的道德和监管框架。
