The National Institutes of Health recently announced a $50 million award to the University of North Texas Health Science Center to lead the coordinating center for the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity, or AIM-AHEAD, program.

The University of North Texas Health Science Center in Fort Worth will lead the multi-institutional coordinating center, which brings together experts in community engagement, artificial intelligence/machine learning (AI/ML), health equity research, data science training, and data infrastructure.

AIM-AHEAD

AIM-AHEAD was created to close the gaps in the AI/ML field, which currently lacks diversity in its researchers and in data, including electronic health records (EHRs). 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. This program seeks to increase the participation and engagement of the researchers and communities that are currently underrepresented in AI/ML modeling and applications through mutually beneficial partnerships.

The AIM-AHEAD Coordinating Center’s initial charge will be to build a consortium of partners and engage with stakeholders. The two-year planning, assessment and capacity building award will identify priority research aims in health equity and AI/ML, as well as the training and infrastructure needed to support these.

Meet the AIM-AHEAD Coordinating Center Team

Leadership Core

Lead: Jamboor K. Vishwanatha, Ph.D.
University of North Texas Health Science Center in Fort Worth

Toufeeq Ahmed, Ph.D.
Vanderbilt University Medical Center

Bettina Beech, Dr.P.H.
University of Houston

Harlan P. Jones, Ph.D.
University of North Texas Health Science Center in Fort Worth

Spero Manson, Ph.D.
University of Colorado-Anschutz Medical Center in Aurora

Keith Norris, M.D., Ph.D.
University of California, Los Angeles

Anil Shanker, Ph.D.
Meharry Medical College in Nashville, Tennessee

Herman Taylor, M.D.
Morehouse School of Medicine in Atlanta, Georgia

Roland J. Thorpe, Jr., Ph.D.
Johns Hopkins University in Baltimore, Maryland

University of North Texas Health Science Center and a team of regional partners will recruit and build a consortium of partners to enhance the inclusivity of data used for AI/ML and the diversity of leadership in AI/ML.   

Data Science Training Core

Lead: Legand L. Burge, Ph.D.
Howard University in Washington, D.C.

The data science training core will implement training opportunities in data science and health equity research, large scale data analysis and management, cloud computing, and other areas to increase AI/ML capabilities.

Infrastructure Core

Co-lead: Alex J. Carlisle, Ph.D.
National Alliance Against Disparities in Patient Health in Woodbridge, Virginia

Co-lead: Paul Avillach, M.D., Ph.D.
Harvard Medical School in Boston, Massachusetts

Co-lead: Bradley A. Malin, Ph.D.
Vanderbilt University Medical Center in Nashville, Tennessee

The infrastructure core 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 and Research Core

Lead: Jon Puro, M.P.A.
OCHIN in Portland, Oregon

OCHIN and multi-disciplinary partners will use EHRs, image data, social determinants of health data, and more to develop and enhance AI/ML algorithms and apply AI/ML approaches in health equity research. This work seeks to illuminate underlying issues in health systems that need to be addressed to improve health for diverse communities. Healthcare should encompass the spectrum of health and disease from prevention, diagnoses, treatments, and implementation strategies.

To learn more about the AIM-AHEAD program, visit https://datascience.nih.gov/artificial-intelligence/aim-ahead.

Press Release

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