Yu Lanyi, Zhai Xiaomei
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Center for Bioethics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Center for Bioethics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Public Health. 2024 Sep;234:77-83. doi: 10.1016/j.puhe.2024.05.029. Epub 2024 Jul 3.
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.
Thematic analysis.
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.
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.
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.
人工智能正在重塑健康与医学领域，尤其是其在解决低收入和中等收入国家（LMICs）健康差距方面的潜力。然而，人工智能的使用存在若干相关问题，这些问题可能会降低其影响力，并有可能加剧全球健康差距。本研究提出了低收入和中等收入国家在人工智能部署中面临的关键问题。
主题分析。
检索了PubMed、Scopus、Embase和Web of Science数据库，从其创建之日至2023年9月，使用了“人工智能”、“低收入和中等收入国家”、“伦理∗”和“全球健康”等检索词。在初次检索前后通过滚雪球式参考文献进行了额外检索。最终的研究是根据它们与本文主题的相关性来选择的。
在审阅了378篇文章后，最终分析纳入了14项研究。引入了一个名为“人工智能部署悖论”的概念，以关注在低收入和中等收入国家使用人工智能解决健康差距所面临的挑战，并确定了以下三个类别：（1）数据匮乏与背景转变；（2）成本效益与健康公平；（3）新技术殖民化与潜在剥削。
全球健康、人工智能与伦理考量之间的关系是一个需要系统研究的领域。依赖存在结构偏差的健康数据且在没有系统伦理考量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经过深思熟虑的伦理和监管框架。
