PDF(Pubmed)
Abstract：
BACKGROUND: Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities.
OBJECTIVE: This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them.
METHODS: We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies.
RESULTS: In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance.
CONCLUSIONS: Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.
OBJECTIVE: This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them.
METHODS: We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies.
RESULTS: In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance.
CONCLUSIONS: Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.
摘要：
背景：新兴的人工智能（AI）应用程序具有改善健康状况的潜力，但它们也可能延续或加剧不平等。
目的：本综述旨在全面概述与使用AI应用程序有关的健康公平性问题，并确定为解决这些问题而提出的策略。
方法：我们搜索了PubMed，WebofScience,IEEE（电气和电子工程师协会）Xplore数字图书馆，ProQuest美国新闻流，学术搜索完成，美国食品和药物管理局（FDA）网站，和ClinicalTrials.gov，以确定2014年至2021年发表的与人工智能和健康公平相关的学术和灰色文献，以及2020年和2021年COVID-19大流行期间与人工智能和健康公平相关的其他文献。如果文献确定了至少一个股票问题以及解决该问题的相应策略，则文献有资格纳入我们的评论。组织和综合股权问题,我们采用了一个四步人工智能应用框架：背景上下文，数据特征,模型设计,和部署。然后，我们创建了问题和策略之间联系的多对多映射。
结果：在660个文档中，我们确定了18个股权问题和15个解决这些问题的策略。与数据特征和模型设计相关的公平问题是最常见的。建议改善公平性的最常见策略是改善数据的数量和质量，评估应用程序引入的差异，增加模型报告和透明度，让更广泛的社区参与人工智能应用程序开发，改善治理。
结论：利益相关者应在规划时审查我们对权益问题和策略的多对多映射，发展,并在医疗保健中实施人工智能应用，以便他们能够制定适当的计划，以确保受其产品影响的人群的公平性。人工智能应用程序开发人员应该考虑采用以公平为重点的清单，和监管机构，如FDA应该考虑要求他们。鉴于我们的审查仅限于在线发布的文档，开发人员可能对我们无法识别的其他问题和策略有未发表的知识。
目的：本综述旨在全面概述与使用AI应用程序有关的健康公平性问题，并确定为解决这些问题而提出的策略。
方法：我们搜索了PubMed，WebofScience,IEEE（电气和电子工程师协会）Xplore数字图书馆，ProQuest美国新闻流，学术搜索完成，美国食品和药物管理局（FDA）网站，和ClinicalTrials.gov，以确定2014年至2021年发表的与人工智能和健康公平相关的学术和灰色文献，以及2020年和2021年COVID-19大流行期间与人工智能和健康公平相关的其他文献。如果文献确定了至少一个股票问题以及解决该问题的相应策略，则文献有资格纳入我们的评论。组织和综合股权问题,我们采用了一个四步人工智能应用框架：背景上下文，数据特征,模型设计,和部署。然后，我们创建了问题和策略之间联系的多对多映射。
结果：在660个文档中，我们确定了18个股权问题和15个解决这些问题的策略。与数据特征和模型设计相关的公平问题是最常见的。建议改善公平性的最常见策略是改善数据的数量和质量，评估应用程序引入的差异，增加模型报告和透明度，让更广泛的社区参与人工智能应用程序开发，改善治理。
结论：利益相关者应在规划时审查我们对权益问题和策略的多对多映射，发展,并在医疗保健中实施人工智能应用，以便他们能够制定适当的计划，以确保受其产品影响的人群的公平性。人工智能应用程序开发人员应该考虑采用以公平为重点的清单，和监管机构，如FDA应该考虑要求他们。鉴于我们的审查仅限于在线发布的文档，开发人员可能对我们无法识别的其他问题和策略有未发表的知识。
