Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

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 Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare

“…Our roadmap supports existing literature on various strategies to mitigate bias in machine learning through the adaptation of a systemic approach promoting health equity, from service design to end-stage maintenance [ 29 ]. An analysis of 660 documents identified 15 strategies addressing 18 equity issues, such as fostering diversity, improving the quality and quantity of data as well as using equity-focussed checklists, guidelines and tools [ 30 ]. Many recommendations of our roadmap support this analysis; however, it also allows conversational AI implementers to recognise and predict specific issues related to individual stages of the conversational AI lifecycle, such as its termination.…”

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach

“…Our work builds on and can be incorporated into current AI and ML ethics and equity frameworks and policies within and outside of the United States, focused on improving population health through broad community involvement in AI and ML application development [ 17 , 36 - 38 ]. This includes, but is not limited to, the National Institutes of Health’s policies and programs on AI and ML application development in health research; policy developments undertaken by the US Senate Health, Education, Labor, and Pensions Committee; the National Academy of Medicine’s Artificial Intelligence Code of Conduct project; the European Commission’s Guidelines for Trustworthy AI; Asilomar AI Principles; and lastly and importantly, a groundbreaking and recent US White House Executive Order explicitly supporting the mission of the AIM-AHEAD [ 36 , 39 - 42 ].…”

Artificial Intelligence Integration in Healthcare: Perspectives and Trends in a Survey of U.S. Health System Leaders

“…that such recommendations could exacerbate existing disparities or create new ones [9][10][11][12][13] . Second, large language models (LLMs), which involve training deep learning algorithms on billions of words and phrases to mimic natural human language abilities 14 , have rapidly been adopted by technology experts and novices alike.…”