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Commentary 

Advancing Health Equity and Biomedical 
Researcher Diversity: A New AIM-AHEAD 
Consortium 
Anil Shanker1,2,3,4,* 

1 Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, School of Medicine, 
Meharry Medical College, Nashville, TN, USA 
2 Host–Tumor Interactions Research Program, Vanderbilt-Ingram Comprehensive Cancer Center, 
Vanderbilt University School of Medicine, Nashville, TN, USA  
3 Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University School of 
Medicine, Nashville, TN, USA 
4 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University, Nashville, TN 

*Corresponding author:  ashanker@mmc.edu 

ABSTRACT 
Despite widespread knowledge regarding racial and ethnic health disparities, little has changed over the 
last decades. A creative, inclusive, and competitive biomedical research workforce is the foundation for 
turning discovery into health for all. To date, the expertise for advancing data-driven medicine based on 
artificial intelligence and machine learning (AI/ML) approaches has resided in majority-oriented 
institutions with little demonstrated experience in engaging minority-serving institutions or communities. 
Lack of diversity of both data and researchers runs the risk of creating and perpetuating harmful biases 
in the analytical algorithms, practice, and outcomes, thus fostering continued health disparities and 
inequities. Thus, the National Institutes of Health recently launched an Artificial Intelligence and Machine 
Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program. This 
two-year planning, assessment and capacity building program will be led by the AIM-AHEAD 
Coordinating Center comprised of a consortium of institutions and organizations that have a mission to 
serve minorities and underrepresented or underserved communities impacted by health disparities. This 
AIM-AHEAD research and development program seeks to illuminate underlying issues in health systems 
and research endeavors that need to be addressed to improve health for diverse communities. 

KEYWORDS: Equity, AIM-AHEAD, Health, Diversity, Researcher 

Citation: Shanker A (2022) Advancing Health Equity and Biomedical Researcher Diversity: A New AIM-
AHEAD Consortium. Cancer Health Disparities 7:e1-4. doi:10.9777/chd.2021.1003 
 
 
  



 
 
 
 
 

 
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Commentary 

Despite widespread knowledge regarding racial 
and ethnic health disparities, little has changed over 
the last 40 years. Similarly, consistent evidence 
documents the benefits of workforce diversity 
across multiple disciplines including science and 
healthcare. Yet, African Americans, Hispanics, 
American Indians/Alaskan Natives, and Native 
Hawaiians/Pacific Islanders, and other minorities, 
including rural populations and persons with 
disabilities, continue to receive higher degrees and 
academic appointments in science, technology, 
engineering and mathematics (i.e., STEM) at rates 
substantially lower than their representation in the 
US population. Recommendations from the 
Advisory Committee to the National Institutes of 
Health (NIH) Director Working Group on Diversity 
in the Biomedical Research Workforce emphasize 
evidence-based and theory-informed strategies to 
increase diversity in the biomedical and health 
professional workforce. A creative, inclusive, and 
competitive biomedical and behavioral research 
workforce is the foundation for turning discovery 
into health for all. Importantly, the widespread 
adoption of electronic health records (EHR) has 
ushered in a new age of data-driven medicine, with 
the emergence of novel methods such as artificial 
intelligence, machine learning (AI/ML), and 
reinforcement learning. These new methods hold 
promise to provide new insights, derived from 
patient and other data, to improve health 
outcomes. To date, the expertise and leadership for 
advancing AI/ML approaches has resided in 
majority-oriented institutions, led by faculty with 
little demonstrated experience or interest in 
engaging minority-serving institutions, 
investigators, or communities. Addressing this void 
requires transformative approaches that cannot be 
grounded in the same systems that have failed to 
generate solutions.  

Therefore, a new Artificial Intelligence and Machine 
Learning Consortium to Advance Health Equity and 

Researcher Diversity (AIM-AHEAD) program was 
launched by the NIH on September 17, 2021. This 
program will establish mutually beneficial, 
coordinated, and trusted partnerships to enhance 
the participation and representation of researchers 
and communities currently underrepresented in the 
development of AI/ML models and improve the 
capabilities of this emerging technology, beginning 
with EHR and extending to other diverse data to 
address health disparities and inequities. The rapid 
increase in the volume of data generated through 
EHR and other biomedical research studies presents 
opportunities for developing new approaches for 
biomedical research and improving healthcare. 
Many challenges hinder the use of AI/ML 
technologies, such as the cost, capability for 
widespread operational and research application, 
and access to appropriate infrastructure, resources, 
and training. Additionally, lack of diversity of both 
data and researchers runs the risk of creating and 
perpetuating harmful biases in the analytical 
algorithms, practice, and outcomes, thus fostering 
continued health disparities and inequities. Many 
underrepresented and underserved communities, 
often disproportionately affected by diseases and 
health conditions, present untapped potential to 
contribute expertise, data, and strategies to inform 
the field on the most urgent research questions. But 
they lack funding, infrastructure, and training to 
apply AI/ML approaches to pertinent research 
questions. 

The two-year planning, assessment and capacity 
building AIM-AHEAD award of $100 million will 
provide the much needed impetus to the cause. 
The University of North Texas Health Sciences 
Center at Fort Worth (UNTHSC) will lead the AIM-
AHEAD Coordinating Center to execute this federal 
research and development program. The 
Coordinating Center is a consortium of institutions 
and organizations that have a mission to serve 
minorities and other under-represented or 



 
 
 
 
 

 
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Commentary 

underserved communities impacted by health 
disparities. The Coordinating Center is comprised of 
four main cores.  

The Leadership/Administrative Core will be led by 
Jamboor K. Vishwanatha, Ph.D., and Harlan P. 
Jones, Ph.D., at UNTHSC along with a team of 
Principal Investigators to lead regional hubs: Bettina 
Beech, Dr.P.H., at University of Houston, Spero 
Manson, Ph.D., at University of Colorado-Anschutz 
Medical Center, Keith Norris, M.D., Ph.D., at 
University of California, Los Angeles, Anil Shanker, 
Ph.D., at Meharry Medical College, Herman Taylor, 
M.D., at Morehouse School of Medicine, and 
Roland J. Thorpe, Jr., Ph.D., at Johns Hopkins 
University. Toufeeq Ahmed, Ph.D., at Vanderbilt 
University Medical Center will lead the 
Communication and Dissemination hub. The 
Leadership Core will recruit consortium members 
and coordinate partnerships, stakeholder 
engagement, and outreach to enhance the diversity 
of researchers in AI/ML-related field, with emphasis 
on health disparities. The inclusion of Historically 
Black Colleges and Universities, and Asian 
American, Native American, Pacific Islander, and 
Hispanic serving institutions in the Leadership Core 
highlight commitment to minority interests. 

The Leadership Core will also coordinate with three 
AIM-AHEAD technical cores of training, research, 
and infrastructure in executing this program. The 
Data Science Training Core will be led by Legand L. 
Burge, Ph.D., at Howard University. The 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 expertise. The Data 
and Research Core will be led by Jon Puro, M.P.A., 
at Oregon Community Health Information Network. 
The research core will determine and address 
research priorities and needs in linking and 
preparing multiple sources and types of research 

data to form an inclusive basis for AI/ML use cases 
that will inform on strategies and approaches to 
ameliorate health disparity. This may include 
facilitating the extraction and transformation of 
data from EHR, image data, social determinants of 
health data, and more to develop AI/ML algorithms 
for application to health equity research. The 
Infrastructure Core will be led by Alex J. Carlisle, 
Ph.D., at National Alliance Against Disparities in 
Patient Health, with co-leads Paul Avillach, M.D., 
Ph.D., at Harvard Medical School, and Bradley A. 
Malin, Ph.D., at Vanderbilt University Medical 
Center. 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 and 
governed by individual institutions in an 
environment preserving privacy and autonomy. 

Building a consortium of right partners and key 
stakeholders is paramount to planning, assessment 
and capacity building to advance health equity and 
researcher diversity. This transformative AIM-
AHEAD program seeks to illuminate underlying 
issues in health systems and research endeavors 
that need to be addressed to improve health for 
diverse communities. As an outcome of this 
program, it is hoped that healthcare will encompass 
the spectrum of health and disease from 
prevention, diagnoses, treatments, and 
implementation strategies for all. 

Acknowledgements 
The author is thankful to the editorial board of 
Cancer Health Disparities for inviting this article. 

Funding 
This author is, in part, funded by the National 
Institutes of Health (NIH) Agreement No. 
1OT2OD032581-01. The views and conclusions 
contained in this Commentary are those of the 
author and should not be interpreted as 



 
 
 
 
 

 
www.companyofscientists.com/index.php/chd e4 Cancer Health Disparities 

Commentary 

representing the official policies, either expressed or 
implied, of the NIH. The author is also supported by 
funds from the NIH grants U54 CA163069, and SC1 
CA182843.  

Conflict of interest statement 
The author declares that no competing or conflict 
of interests exist. The funders had no role in content 
design, writing of the manuscript, or decision to 
publish. 

Authors’ contributions 
Conception, design and writing: AS 


	Acknowledgements
	Funding
	Conflict of interest statement
	Authors’ contributions

