This post offers a curated collection of articles, toolkits, white papers, and/or other resources on how Artificial Intelligence (AI) has the potential to advance health equity across diverse patient populations through improved access and resource optimization. However, ethical concerns and potential risks must be addressed to fully realize its benefits. Click on the toggle for any reference to view a brief summary of the document, its source, and an active link for access.
Arora, H. (2024, March 26). NIHCM: The power and promise of AI for health equity
Overview: This presentation highlights the transformative potential of AI in healthcare, particularly its ability to improve health equity through enhanced access, personalized treatments, and resource optimization. However, realizing this promise requires addressing ethical concerns and minimizing potential risks. The slides outline strategies to leverage AI’s efficiency and insights while maintaining human oversight to uphold ethical standards. By focusing on bias mitigation and equitable implementation, AI technologies can enhance healthcare delivery, improve patient outcomes, and promote health equity for all stakeholders.
Arora, H. (2024, March 26). NIHCM: The power and promise of AI for health equity [PowerPoint slides]. NIHCM Foundation. https://nihcm.org/assets/articles/Himanshu-Aroras-Slides-032624.pdf
Clark, R. C., Wilkins, C. H., Rodriguez, J. A., Preininger, A. M., Harris, J., DesAutels, S., Karunakaram, H., Rhee, K., Bates, D. W., & Dankwa-Mullan, I. (2021). Health care equity in the use of advanced analytics and artificial intelligence technologies in primary care
Abstract: The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.
Clark, R. C., Wilkins, C. H., Rodriguez, J. A., Preininger, A. M., Harris, J., DesAutels, S., Karunakaram, H., Rhee, K., Bates, D. W., & Dankwa-Mullan, I. (2021). Health care equity in the use of advanced analytics and artificial intelligence technologies in primary care. Journal of General Internal Medicine, 36, 3188-3193. https://doi.org/10.1007/s11606-021-06846-x
Sambamoorthi, U., (2024, March). AI for community design, data, and decisions: Health equity through artificial intelligence (AI) and machine learning (ML)
Overview: The presentation defines key terms, explores AI bias and fairness, and outlines strategies to leverage AI opportunities while addressing challenges to promote health equity in healthcare.
Sambamoorthi, U., (2024, March). AI for community design, data, and decisions: Health equity through artificial intelligence (AI) and machine learning (ML) [PowerPoint slides]. NIHCM Foundation. https://nihcm.org/assets/articles/Usha-Sambamoorthis-Slides-032624.pdf
Vishwanatha, J. K, Christian, A., Sambamoorthi, U., Thompson, E. L., Stinson, K., & Syed, T. A. (2023). Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions
Abstract: Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the “AIM-AHEAD Community Building Convention (ACBC).” A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD’s future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes.
Vishwanatha, J. K, Christian, A., Sambamoorthi, U., Thompson, E. L., Stinson, K., & Syed, T. A. (2023). Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions. PLOS Digital Health, 2(6), e0000288. https://doi.org/10.1371/journal.pdig.0000288