Berdahl Carl Thomas, Baker Lawrence, Mann Sean, Osoba Osonde, Girosi Federico
RAND Corporation, Santa Monica, CA, United States.
Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
JMIR AI. 2023 Feb 7;2:e42936. doi: 10.2196/42936.
Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities.
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.
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.
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.
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 Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.
新兴的人工智能（AI）应用有改善健康状况的潜力，但也可能使不平等现象长期存在或加剧。
本综述旨在全面概述与使用人工智能应用相关的健康公平问题，并确定为解决这些问题而提出的策略。
我们检索了PubMed、科学网、电气与电子工程师协会（IEEE）Xplore数字图书馆、ProQuest美国新闻流、学术搜索完整版、美国食品药品监督管理局（FDA）网站以及ClinicalTrials.gov，以识别2014年至2021年期间发表的与人工智能和健康公平相关的学术文献和灰色文献，以及2020年和2021年新冠疫情期间与人工智能和健康公平相关的其他文献。如果文献至少确定了一个公平问题以及相应的解决策略，则有资格纳入我们的综述。为了组织和综合公平问题，我们采用了一个四步人工智能应用框架：背景情境、数据特征、模型设计和部署。然后，我们创建了问题与策略之间联系的多对多映射。
在660篇文献中，我们确定了18个公平问题以及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最为常见。为改善公平性而推荐的最常见策略包括提高数据的数量和质量、评估应用引入的差异、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开发以及改善治理。
利益相关者在医疗保健领域规划、开发和实施人工智能应用时，应查看我们的公平问题与策略的多对多映射，以便他们能够制定适当的计划，确保受其产品影响的人群实现公平。人工智能应用开发者应考虑采用以公平为重点的清单，FDA等监管机构应考虑要求他们这样做。鉴于我们的综述仅限于在线发表的文献，开发者可能有我们未能识别的其他问题和策略的未发表知识。
