Request for Information (RFI): Inviting Input to Broaden the Benefits of AI/ML Technologies to Reduce Health Disparities and Inequities and Enhance the Diversity of the AI/ML Workforce
Notice Number:

Key Dates

Release Date:

June 21, 2021

Response Date:
July 09, 2021

Related Announcements


Issued by

Office of The Director, National Institutes of Health (OD)


Through this Request for Information (RFI), the National Institutes of Health (NIH) seeks to understand the needs, interests and opportunities for building and advancing Artificial Intelligence/Machine Learning (AI/ML) approaches using electronic health record (EHR) and other types of data (e.g., genomics, imaging, social determinants of health) to redress health disparities and advance health equity, with an emphasis on providing technical assistance and training on research and implementation of AI/ML to lay ground work for the preparation of new data systems for underrepresented groups.


The rapid increase in the volume of data generated through EHR and other biomedical research presents exciting opportunities for developing and data science approaches (e.g., AI/ML methods) for biomedical research and improving healthcare. Many challenges hinder more widespread use of AI/ML technologies, such as the cost, capability for widespread application, and access to appropriate infrastructure, resources, and training. Additionally, lack of diversity of both data and researchers in the AI/ML field runs the risk of creating and perpetuating harmful biases in its practice, algorithms, and outcomes, thus fostering continued health disparities and inequities. Underrepresented groups, which are oftentimes disproportionately affected by diseases and health conditions, have the potential to contribute expertise, data, diverse recruitment strategies, and cutting-edge science but may lack financial, infrastructural, and training capacity to apply AI/ML approaches to research questions of interest to them.

NIH is committed to leveraging the potential of AI/ML to accelerate the pace of biomedical innovation, while prioritizing and addressing health disparities and inequities. Tackling the complex drivers of health disparities and inequities requires an innovative and transdisciplinary framework that transcends scientific and organizational silos. Mutually beneficial partnerships can be established to increase the participation of currently underrepresented researchers and communities and enhance the capabilities of in AI/ML modeling and application for these communities. NIH envisions a comprehensive initiative to develop and support a consortium of institutions and organizations to build capacity and capability through infrastructure, training to achieve a diverse data science workforce, and access to high-quality, diverse AI/ML-ready data from diverse populations and sources (e.g., EHR, genomics, imaging, social determinants of health data) that will, for example but not limited to:

  • Facilitateregional multi-disciplinary partnerships to create a “network of networks” that integrates data science research networks with community engagement and clinical research networks to form mutually beneficial collaborations and support engagement of underrepresented scientists across the career pipeline. Develop community engagement programs to invigorate the pipeline of data scientist trainees that represent the full diversity of the nation.
  • Foster these multi-disciplinary partnerships to build new, synthetic, or leverage existing datasets(EHR and other types of data), to develop and enhance AI/ML algorithms and apply AI/ML approaches to address health inequities and disparities, to improve healthcare, prevention, diagnoses, treatments, and facilitate intervention and implementation strategies.
  • Enable a federated data and computing infrastructure that enhances the interoperability and where data are maintained, governed, and prepared by individual member institutions to preserve privacy and autonomy while ensuring data interoperability and federated data sharing across the network.

Information Requested:

The RFI will inform the development of an NIH initiative for funding in FY21 that is bold, innovative, and substantiative in broadening the benefits of AI/ML technologies, reducing health disparities and inequities, and enhancing diversity of the AI/ML workforce. NIH invites input from stakeholders such as those in biomedical and computer sciences, clinical and healthcare practice, data science and informatics, privacy and ethics, scientific organizations, patient advocacy groups, and from academia, industry, Federal government, and the general public. NIH is especially interested in comments from under-resourced institutions and communities including, for example, Minority-Serving Institutions, Federally Qualified Health Clinics, major safety-net hospitals, community partners and clinics, academic institutions, Research Centers in Minority Institutions (RCMI), Institutional Development Award (IDeA) awardees and networks, and other institutions and health systems.

NIH encourages organizations (e.g., patient advocacy groups, professional organizations) to submit a single response reflective of the views of the organization or membership as a whole.

In addition to this RFI, and as part of the public engagement process, NIH will hold a virtual stakeholder forum on June, 25, 2021. More information on this forum may be found at

The NIH seeks responses to any or all of, but not limited to, the following topics:

  • Use of AI/ML for health disparities and inequities research. Examples of specific comments include, but are not limited to:
    • Knowledge, experience, and interest in using AI/ML for health disparities and inequities research
    • High priority research topics of interest (e.g., biomedical research,predictive modeling,community-engagement research, implementation science, clinical studies)
    • Types of pilot studies that could inform future health disparities research
  • Infrastructure and resources for AI/ML application and research. Examples of specific comments include, but are not limited to:
    • Current infrastructure available (e.g., local network drive, cloud access)
    • Resources available (e.g., staffing, data management platforms, access to EHR and other types of biomedical research and clinical data, access to existing study populations)
    • Infrastructure and/or resources needed (federated data, cloud computing etc.)
  • Partnerships approaches for AI/ML application. Examples of specific comments include, but are not limited to:
    • Interest in establishing multi-disciplinary partnerships and networks
    • Current partnerships, networks, or initiatives that could be leveraged
    • Types of partnerships or networks desired or needed
    • Strategies to ensure and build trust for substantial and sustaining impact
    • Willingness, interest, or concerns to sharing data and resources
  • Training for AI/ML approaches and health disparities and inequities. Examples of specific comments include, but are not limited to:
    • Training and type of training resources currently available or accessible
    • Level of training needed (e.g., students, early career, late career)
    • Types of training needed (e.g., data science and AI/ML methods, cloud computing, health disparities research, community engagement research and implementation science)
    • Novel approaches to facilitate training
  • Opportunities, challenges, and considerations with using AI/ML to study health disparities and inequities. Examples of specific comments include, but are not limited to:
    • Opportunities for using AI/ML
    • Challenges or limitations to using AI/ML
    • Concerns or needs of special or unique populations
    • Considerations for using AI/ML (e.g., overall purpose, future uses, consent)
  • Any other needs, interests, concerns or information relevant to the purpose of the RFI

How to Submit a Response

All comments must be submitted electronically on the submission website .

Responses must be received by 11:59:59 pm (ET) on July 9, 2021.

Responses to this RFI are voluntary and may be submitted anonymously. You may voluntarily include your name and contact information with your response. If you choose to provide NIH with this information, NIH will not share your name and contact information outside of NIH unless required by law.

Other than your name and contact information, please do not include any personally identifiable information or any information that you do not wish to make public. Proprietary, classified, confidential, or sensitive information should not be included in your response. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. The Government will use the information submitted in response to this RFI at its discretion. Other than your name and contact information,the Government reserves the right to use any submitted information on public websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future funding opportunity announcements.This RFI is for informational and planning purposes only and is not a solicitation for applications or an obligation on the part of the Government to provide support for any ideas identified in response to it. Please note that the Government will not pay for the preparation of any information submitted or for use of that information.

We look forward to your input and hope that you will share this RFI opportunity with your colleagues.


Please direct all inquiries to:

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