Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD)

About the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD)

NIH’s AIM-AHEAD program will establish mutually beneficial and coordinated partnerships to increase the participation and representation of researchers and communities currently underrepresented in the development of AI/ML models and enhance the capabilities of this emerging technology, beginning with electronic health record (EHR) data.

The AI/ML field currently lacks diversity in its researchers and in data, including electronic health record (EHR) data. These gaps pose a risk of creating and continuing harmful biases in how AI/ML is used, how algorithms are developed and trained, and how findings are interpreted. Critically, these gaps can lead to continued health disparities and inequities for underrepresented communities.

Underrepresented communities have untapped potential to contribute new expertise, data, recruitment strategies, and cutting-edge science to the AI/ML field. To close the gaps in the field and to better engage underrepresented communities, the NIH has launched the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program.

This program seeks to increase the participation and representation of the researchers and communities that are currently underrepresented in AI/ML modeling and applications through mutually beneficial partnerships. AIM-AHEAD will also enhance AI/ML capabilities and has four key areas:


AIM-AHEAD will create a “network of networks” through regional, multi-disciplinary partnerships. The goal is to integrate AI/ML-focused, data science research networks with community engagement and clinical research networks to form mutually beneficial collaborations and to engage underrepresented scientists across the career pipeline.


AIM-AHEAD’s multi-disciplinary partners will use new, real world data or synthetic, and existing datasets, such as EHR, image data, and social determinants of health; develop and enhance AI/ML algorithms; and apply AI/ML approaches to address health inequities and disparities. This work may encompass improved healthcare, prevention, diagnoses, and treatments and facilitate intervention and implementation strategies.


AIM-AHEAD will enable a coordinated data and computing infrastructure that enhances the interoperability of large-scale data resources with data that are maintained, governed, and prepared by individual institutions to preserve privacy and autonomy.

Data Science Training

AIM-AHEAD will implement training opportunities in data science and health disparities research, large scale data management, cloud computing, and other areas to increase capabilities in AI/ML analytics.

Stakeholder Engagement Forum

The NIH hosted a virtual stakeholder engagement forum on AIM-AHEAD on June 25. A summary and recording of the event are now available.

Learn more about how AIM-AHEAD was developed. View the original concept clearance report and slides presented at the May 6, 2021, Meeting of the Advisory Committee to the NIH Director.

Contact Information

Please direct questions about AIM-AHEAD to

Questions about the Research Opportunity Announcement should be directed to

This page last reviewed on August 4, 2021