AIM-AHEAD Clinicians Leading Ingenuity IN Al Quality (CLINAQ)

Fellowship Program

Call for Proposals

Key Dates

Solicitation Release Date: May 15, 2024

Application Due Date: June 30, 2024

Application Review Date: July 16, 2024

Earliest Start Date: September 16, 2024



Please create a Help Desk ticket:


Project Team

Herman Taylor (Director)

Keith Norris (Co-Director)

Toufeeq Syed (Co-Director)

Caitlin Rollins (Project Manager)

Chad Evans (Project Manager)



Please refer to the Frequently Asked Questions document before creating a Help Desk ticket:  AIM-AHEAD CLINAQ Fellowship FAQs.


The AIM-AHEAD Clinicians Leading Ingenuity IN Al Quality (CLINAQ) Fellowship Program is a one-year fellowship (September 16, 2024 - August 29, 2025) that seeks to operate in parallel with clinical practice to empower clinicians (defined below) in the field of Artificial Intelligence/Machine Learning (AI/ML). The term clinician encompasses a broad range of professionals including physicians, nurses, physician assistants, therapists, and pharmacists, among others. By fostering an interdisciplinary approach, the CLINAQ Fellowship cultivates a new generation of clinicians who can champion innovation in AI for healthcare. Fellows will select among opportunities to actively participate in the development of AI tools, assess the suitability for AI/ML solutions for specific clinical/workflow challenges, and identify AI/ML approaches that will enhance equity in the delivery of care. The program’s focus is to mitigate biases, enhance cultural competence and address healthcare disparities among diverse patient populations. The program offers courses in AI and Ethics, Clinical AI workshops, mentoring, leadership training and peer networks, all while fostering the development of a community of clinicians from diverse backgrounds with a common interest in AI/ML. By equipping a diverse cohort of clinicians with AI/ML skills and knowledge, the program will enhance the representation of diverse perspectives in healthcare technology. An interdisciplinary, team approach will be emphasized to foster innovation and improve patient outcomes.



The AIM-AHEAD Coordinating Center was established to enhance diversity in the field of artificial intelligence and machine learning (AI/ML), with an emphasis on reducing health disparities and promoting health equity. To achieve this objective, we will engage in a fair, equitable, and transparent process of building a consortium of AI/ML to promote health equity and an inclusive and diverse workforce. Many communities have untapped potential to contribute new expertise, data, recruitment strategies, and cutting-edge science to the AI/ML field. To advance health equity, AIM-AHEAD Coordinating Center (A-CC) seeks to increase participation and engagement through mutually beneficial partnerships, stakeholder engagement, and outreach.


The Coordinating Center consists of four cores: 1) Leadership/Administrative Core; 2) Data Science Training Core; 3) Data and Research Core; and 4) Infrastructure Core. Each core provides distinct resources that are essential to expand and support education, training, and implementation of AI/ML models and research that addresses health disparities and advances health equity. Brief descriptions of the cores are provided below.


Leadership/Administrative Core: Leads the overall A-CC, recruits and coordinates consortium members, project management, partnerships, stakeholder engagement, and outreach to enhance the diversity of researchers in AI/ML related research with an emphasis on health disparities research, and establishes and maintains trusted relationships with groups experiencing health disparities to enhance the diversity of data used in AI/ML research. The Leadership/Administrative Core consist of seven regional hubs (i.e., Central, Northeast, North and Midwest, South Central, Southeast (2), and West) that are designed to engage partners and stakeholders across the United States (U.S.). This core operates as a Pass-Through Entity (PTE) for AIM-AHEAD.


Data Science Training Core: Develops, implements, and assesses data science training curricula to enhance capacity among diverse populations, including underrepresented or underserved groups impacted by health disparities.


Data and Research Core: Determines and addresses 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 illuminate strategies and approaches to ameliorate health disparities. This core's contributions may include facilitating the extraction and transformation of data from electronic health records (EHR) for research use and consideration of social determinants of health as crucial contributors to health outcomes.


Infrastructure Core: Conducts the assessment of data, computing, and software infrastructure models, tools, resources, data science policies, ethical AI, and AI/ML computing models that will facilitate AI/ML and health disparities research; and establishes pilot data and analytic environments to accelerate accomplishment of the overall A-CC aims.


The CLINAQ Fellowship Program seeks to enhance the participation of healthcare professionals in the field of AI/ML within clinical care. This program aims to address significant issues like healthcare disparities, cultural incompetence in AI applications, and biases in AI algorithms. By equipping clinicians with the requisite skills and knowledge in AI, the program endeavors to foster inclusive healthcare solutions and advance medical research and practice. Fellowship program participants will be introduced to the role of AI in medicine and gain sophistication in the evaluation and application of common and emerging AI tools via a combination of virtual and in-person didactic sessions, webinars, and workshops.


Each fellow will be expected to undertake a research project that uses AI to address a specific challenge within their respective clinical fields. Each fellow will identify a problem, develop an AI-based solution, conduct the research, analyze the results and report the findings. Fellows will have mentors to guide them through this process and collaboratively develop AI/ML models focused on health equity within their respective clinical fields. Applicants with minimal AI/ML experience are welcome to apply. However, such applicants are strongly encouraged to find a mentor or collaborator with strong AI/ML expertise who can be listed in their application to assure the alignment of the Program's emphasis on teamwork in solving clinical challenges using AI/ML tools


Each fellow will be expected to produce a poster for presentation at the annual national AIM AHEAD Consortium meeting (August 2025) based on their research project, fellowship experience, observations, changes in practice behavior, and/or implementation of AI/ML in the clinical practice or clinical research settings.


Structured Courses and Trainings:

  • Leadership/Team Science Training

Leadership and team science training for fellows is designed to cultivate essential skills such as critical thinking, decision-making, and effective communication. It includes comprehensive sessions on project management, team building, ethical leadership, and AI governance, alongside workshops aimed at fostering innovation and personal resilience. Training also emphasizes the importance of ethical considerations and regulatory compliance in AI healthcare projects. By integrating real-world applications and providing opportunities for networking and mentorship, the training will equip fellows to 1) form effective collaborations across and beyond their local institutions; 2) lead transformative changes in clinical care; 3) manage diverse teams; and 4) advocate for equitable AI practices, positioning them as future leaders in the intersection of AI and healthcare. 


  • Clinical AI Workshop Series

CLINAQ Fellowship participants will attend a series of dynamic workshops led by esteemed AI/ML researchers who are also clinicians. These workshops are designed to seamlessly blend advanced AI/ML knowledge with practical clinical applications, enhancing the fellows' ability to integrate innovative technologies into healthcare settings. Each workshop consists of a one-hour lecture followed by a 30-minute interactive Q&A session, allowing fellows to delve deeper into specific issues and seek personalized advice on their projects. Key objectives include deepening the fellows’ understanding of AI/ML deployment in clinical practice, fostering innovative problem-solving skills, and facilitating direct engagement with industry leaders to enhance their professional networks and project outcomes. Through these workshops, fellows will gain critical insights into the ethical and practical challenges of AI/ML in healthcare, receive guidance on refining their methodologies, and build valuable professional relationships that extend beyond the fellowship duration. To enhance the relevance and impact of each workshop, experts will review the abstracts of the fellows' research projects beforehand. The expert guidance will prepare fellows to customize their presentations and discussions to directly address the specific challenges and topics pertinent to their proposed areas of research.


  • Mentorship

This CLINAQ mentorship program is designed to establish a robust mentor/mentee relationship that leverages the mentor's expertise extensively. For candidates lacking AI/ML skills, mentors will play a crucial role in providing access to necessary resources, ensuring that all fellows, regardless of their initial skill level, can fully engage with and benefit from the program.


  • Community Based System Dynamics Training

This 2-day (in-person) workshop will introduce fellows to principles of Community-Based System Dynamics (CBSD) to engage communities, organizations, and other actors in health systems to frame issues and conceptualize the underlying systems using system dynamics with an emphasis on bringing richer causal theories of social context to inform the assessment and development of AI/ML data-driven applications. CBSD is a participatory method that directly involves communities and organizations in understanding and addressing issues in complex systems by combining the system dynamics approach (including causal mapping and formal modeling with computer simulation) with participatory research methods. The emphasis will be on the development and appraisal of causal feedback computational models of health disparities that subsequently be used to inform the development of AI/ML models.


The system dynamics models will be developed using Stella Online (free version available) and Stella Architect (workshop license provided at no cost), commercial software that supports causal mapping, formal development of system dynamics computational models, algorithms for analyzing the loop dominance dynamics, and creating and deploying interactive online model interfaces.


Structural violence (Galtung, 1969) is an umbrella term referring to the preventable morbidity and mortality associated with cumulative exposure to elevated risk of marginalization that encompasses racism, sexism, classism, ageism, etc. Day 1 will focus on framing structural violence and health inequities as complex adaptive systems, providing an overview of systems science methods in public health, and developing systems thinking/system dynamics skills through a series of small group exercises drawing on principles CBSD. At the end of Day 1, clinical fellows will be able to:

        1. Identify and distinguish 3 systems science methods for understanding complex adaptive systems.
        2. Frame/prioritize dynamic problems or issues along with the underlying feedback system.
        3. Describe a benefit from moving toward a more formal computer simulation and analysis.
        4. Summarize the argument for how CBSD can help address the bias in AI/ML models that ignore feedback mechanisms. 


Day 2 will focus on applied applications on issues identified by Clinical Fellows. Clinical fellows will identify and share topics of interest for a potential application and form project teams of 2-4 persons. Working in project teams, clinical fellows will conceptualize the problem/issue as a set of longitudinal or time series patterns (i.e., reference modes); describe the idealized system, the expanded boundary of the system to include factors that would influence key variables; and the feedback effects likely driving potential outcomes of interest. Teams will then present and receive feedback on their projects and next steps. At the end of Day 2, clinical fellows will be able to:

        1. Develop a set of reference modes for a practical clinical application and enter these into a Stella system dynamics model.
        2. Conceptualize a system using a causal loop diagram or stock and flow diagram and create a view of the system using Stella Architecture.
        3. Present the evolution of a system/systems awareness by “unfolding” the model in a Stella interface using a story.
        4. Identify at least one system archetype that might cover (and solve) the problem or issue at hand. 
        5. List the next steps for improving the causal modeling to gather more information to inform the development of an AI/ML algorithm.

The CBSD training will offer substantial benefits to fellows in the program focused on artificial intelligence (AI) in healthcare, particularly in understanding complex systems, engaging stakeholders, and fostering collaborative problem-solving. By incorporating CBSD, fellows will gain valuable insights into the systemic drivers of healthcare disparities and develop AI solutions that are robust, culturally competent, and tailored to the needs of diverse populations. This participatory approach not only enhances the design and implementation of AI technologies but also ensures that these innovations are socially acceptable and effectively address the root causes of health inequities. Through CBSD, fellows will adopt a holistic systems thinking perspective, crucial for anticipating and mitigating unintended consequences of AI interventions in healthcare, thereby contributing positively to health systems and promoting equitable health outcomes. This community-based system science approach will be utilized by fellows and applied to design AI/ML interventions in the clinical setting. For example, fellows might use CBSD to understand the factors influencing medication adherence in a specific community and then design an AI intervention that addresses those factors.



Asynchronous Courses and Trainings (via AIM-AHEAD Connect):

  • AI/ML for Frontline Healthcare Workers - Link

Designed with Frontline Healthcare Workers in mind, this asynchronous course offers a unique opportunity to unlock the potential of Artificial Intelligence (AI) and Machine Learning (ML) without the need for coding expertise. While many courses emphasize coding skills, our approach is different. We have identified the needs specific to frontline healthcare workers through a nationwide survey and targeted interviews to inform this curriculum. The resounding consensus is clear: you want practical knowledge that directly enhances your day-to-day work. No coding experience is required for this course. This course will guide you as you walk through real-world healthcare use cases applying cutting-edge technology and approaches. You will explore the world of AI/ML applications on healthcare and health equity to gain a deep understanding of the technology, capabilities, limitations, and potential impact on patient care.


  • AIM-AHEAD Introductory Course: AI for Health Care Applications - Link

This self-guided course presents basic concepts underlying AI/ML as applied to health care data. The course content is organized as a series of self-contained python notebooks, each with an accompanying recorded tutorial and example datasets. The notebooks provide a written narrative of the python libraries that are used to clean/build training sets, define AI model architecture, and evaluate model performance. Help sessions with some live presentations will also be provided.


  • Unconscious Bias - Link

The Unconscious Bias Course will help fellows address their personal or unconscious biases, help them recognize microaggressions, provide a solutions toolkit, develop their self-awareness, and discuss bias and disparities in medicine and healthcare. A certificate is awarded upon completion of these modules. The five modules may be completed" or "The five-module course may be completed all at once or each module may be taken separately. There is no set completion window, but all five modules must be completed to receive the certificate. Each module takes approximately 20 minutes to complete.


  • The fellowship training schedule can be found here - Link

Expected Outcomes

  • The establishment of a supportive network of clinicians and AI experts dedicated to promoting diversity and inclusion in clinical care and research.
  • A comprehensive understanding of current AI development in healthcare and its potential impact on health equity and AI biases.
  • Actionable recommendations and strategies for increasing URM clinician participation in the development of AI to address health disparities.
  • An appreciable increase in the number of clinicians who are skilled in integrating AI algorithm development with culturally sensitive and equitable AI solutions.


AIM-AHEAD North Stars 

The CLINAQ fellowship program aligns to all AIM-AHEAD North Stars by 1) diversifying the AI/ML workforce through specialized clinical training, 2) increasing public literacy and engagement via community education efforts by fellows, 3) promoting development of  AI/ML solutions to address specific minority health disparities in focus areas like behavioral health and cancer, and cardiometabolic health 4) building capacity in clinics and neighborhoods for responsible community-centric AI deployment. With a curriculum grounded in participatory methods, the fellowship prepares clinicians to ethically apply AI to improve minority health outcomes. Additional information about the North Stars can be found on the AIM-AHEAD website (


Awardee Expectations 

  • Fellows will be required to actively participate in all scheduled training sessions, workshops, and seminars. These educational components are designed to provide a comprehensive understanding of AI, machine learning, data science, and their applications in healthcare.
  • Each fellow will be expected to undertake a research project that addresses a specific challenge in healthcare using AI. This involves identifying a problem, developing an AI-based solution, conducting the research, and analyzing the results. A list of example research projects can be found here - Link 
  • Fellows will have mentors to guide them through this process. See AIM-AHEAD Mentors section below for details.
  • Fellows will be responsible for presenting results of their mentored project (oral or poster) at the AIM-AHEAD annual meeting in August 2025.
  • Collaboration is a cornerstone of the fellowship program. Fellows are expected to work alongside their peers, mentors, and other professionals in the field to share knowledge, solve problems, and contribute to each other's learning and development.
  • Fellows will have opportunities to present their research findings and projects to a wider audience, including at conferences, seminars, and through publications. This is crucial for disseminating knowledge and contributing to the broader discourse on AI in healthcare.
  • Beyond technical skills, fellows are encouraged to develop their professional skills, including leadership, communication, and networking. Participation in relevant professional development activities is expected.
  • Participating in the fellowship program also involves contributing to the community of fellows by sharing knowledge, providing support, and participating in peer mentoring when possible.
  • Regular reporting on the progress of the fellow’s research and projects will be required. This expectation includes meeting the milestones set at the beginning of the fellowship and participating in evaluations to assess the impact of the fellow’s work.


To be eligible for this program, the applicant must be a practicing clinician with an MD, DO, DDS/DMD, MD/PhD, PA, DNP/FNP, RN/PhD or similar degrees -- who is active in direct clinical care of patients. Additional professional designations may be considered on a case-by-case basis.

  • Applicants with minimal or no AI/ML experience are encouraged to apply, but will need to identify an AI/ML mentor/collaborator with AI/machine learning expertise in their application. Qualifications of such pre-selected mentors will be carefully evaluated in the application review process.


***Accepted applicants must be able to submit a W-9 tax form


Eligible Organizations

Higher Education Institutions

    • Public/State Controlled Institutions of Higher Education
    • Private Institutions of Higher Education


We especially welcome applicants from the following types of Higher Education Institutions to apply for NIH support as Public or Private Institutions of Higher Education:

    • Hispanic-serving Institutions
    • Historically Black Colleges and Universities (HBCUs)
    • Tribally Controlled Colleges and Universities (TCCUs)
    • Alaska Native and Native Hawaiian Serving Institutions
    • Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)


Nonprofits Other Than Institutions of Higher Education

    • Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
    • Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)


For-Profit Organizations

    • Small Businesses
    • For-Profit Organizations (Other than Small Businesses)



    • State Governments
    • County Governments
    • City or Township Governments
    • Special District Governments
    • Indian/Native American Tribal Governments (Federally Recognized)
    • Indian/Native American Tribal Governments (Other than Federally Recognized)
    • Eligible Agencies of the Federal Government
    • U.S. Territory or Possession



    • Independent School Districts
    • Public Housing Authorities/Indian Housing Authorities
    • Native American Tribal Organizations (other than federally recognized tribal governments)
    • Faith-based or Community-based Organizations
    • Regional Organizations


The sponsoring institution must:

    • Assure support for the proposed program applicant. Appropriate institutional commitment to the program includes the provision of adequate staff, facilities, and educational resources that can contribute to the planned program.
    • Be able to obtain an IRB determination for studies of data from human subjects (private IRB reviews are permitted for institutions lacking an IRB)
    • Sign off on Data Use Agreements and Data Sharing Agreements


Institutions with existing Ruth L. Kirschstein National Research Service Award (NRSA) institutional training grants (e.g., T32) or other federally funded training programs may apply for a research education grant provided that the proposed educational experiences are distinct from those training programs receiving federal support. In many cases, it is anticipated that the proposed research education program will complement ongoing research training occurring at the applicant institution.


A note on Organizational Compliance:

    • Applicant organizations must be able to obtain an IRB determination (even Not Humans Subjects/Exempt Research). Private IRB reviews allowable.
    • An institutional official must be authorized to sign Data Use Agreements / Data Sharing Agreements


Foreign Institutions

Non-domestic (non-U.S.) Entities (Foreign Institutions) are not eligible to apply

Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.

Foreign components, as defined in the NIH Grants Policy Statementare not allowed.

Selection Criteria

The CLINAQ fellowship aims to specifically target clinicians, emphasizing clinical experience, expertise in patient care, and a demonstrated capacity to integrate AI/ML technologies into clinical practice. Key criteria include:

    • Applicants should demonstrate substantial clinical experience, showcasing a deep understanding of patient care dynamics, diagnosis, and treatment processes in their specific medical specialty.
    • Applicants should demonstrate depth of clinical experience in relevant specialties, innovative use of technology in patient care, and a clear vision for applying AI/ML to enhance clinical outcomes and address health disparities.
    • Special consideration may be given to clinicians whose areas of expertise align with key medical fields where AI/ML integration is still emerging and holds potential for groundbreaking discoveries or significant impacts.
    • Clinicians with little to no experience in AI/ML are also encouraged to apply. The program is structured to provide foundational knowledge and gradually build up to more complex applications, making it suitable for learners at all levels.
    • Clinicians applying to the fellowship should demonstrate a keen interest in developing their AI/ML skills to address clinical challenges. A clear vision of how they intend to integrate these technologies into their practice is crucial.

Applicant Diversity

The AIM-AHEAD Coordinating Center supports applicants from diverse backgrounds, including those from groups traditionally underrepresented in the biomedical, behavioral, and clinical research workforce, as described in the NIH Notice of Interest in Diversity (NOT-OD-20-031), are encouraged.


A. Individuals from racial and ethnic groups that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis (see data at and the report Women, Minorities, and Persons with Disabilities in Science and Engineering). The following racial and ethnic groups have been shown to be underrepresented in biomedical research: Blacks or African Americans, Hispanics or Latinos, American Indians or Alaska Natives, Native Hawaiians and other Pacific Islanders. In addition, it is recognized that underrepresentation can vary from setting to setting; individuals from racial or ethnic groups that can be demonstrated convincingly to be underrepresented by the grantee institution should be encouraged to participate in NIH programs to enhance diversity. For more information on racial and ethnic categories and definitions, see the OMB Revisions to the Standards for Classification of Federal Data on Race and Ethnicity ( ).


B. Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities, as described in the Americans with Disabilities Act of 1990, as amended. See NSF data at,


C. Individuals from disadvantaged backgrounds, defined as those who meet two or more of the following criteria:

    1. Were or currently are homeless, as defined by the McKinney-Vento Homeless Assistance Act (Definition:;
    2. Were or currently are in the foster care system, as defined by the Administration for Children and Families (Definition:;
    3. Were eligible for the Federal Free and Reduced Lunch Program for two or more years (Definition:;
    4. Have/had no parents or legal guardians who completed a bachelor’s degree (see;
    5. Were or currently are eligible for Federal Pell grants (Definition: ;
    6. Received support from the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) as a parent or child (Definition:
    7. Grew up in one of the following areas: a) a U.S. rural area, as designated by the Health Resources and Services Administration (HRSA) Rural Health Grants Eligibility Analyzer (, or b) a Centers for Medicare and Medicaid Services-designated Low-Income and Health Professional Shortage Areas (qualifying zip codes are included in the file).


Students from low socioeconomic (SES) status backgrounds have been shown to obtain bachelor’s and advanced degrees at significantly lower rates than students from middle and high SES groups (see, and are subsequently less likely to be represented in biomedical research. For background see Department of Education data at:;;


D. Literature shows that women from the above backgrounds (categories A and B) face particular challenges at the graduate level and beyond in scientific fields. (See, e.g., From the NIH: A Systems Approach to Increasing the Diversity of Biomedical Research Workforce


Women have been shown to be underrepresented in doctorate-granting research institutions at senior faculty levels in most biomedical-relevant disciplines, and may also be underrepresented at other faculty levels in some scientific disciplines (See data from the National Science Foundation National Center for Science and Engineering Statistics: Women, Minorities, and Persons with Disabilities in Science and Engineering, special report available at , especially Table 9-23, describing science, engineering, and health doctorate holders employed in universities and 4-year colleges, by broad occupation, sex, years since doctorate, and faculty rank).

Review Criteria

In assigning priority scores, reviewers will apply to the following criteria 1 and 2 the standard NIH 1-9 scoring range, where a score of 1 indicates highest enthusiasm, and a score of 9 indicates lowest enthusiasm, based on NIH Simplified Review Framework - 


Criterion 1: Importance of the Research (Significance, Innovation), scored 1-9

Criterion 2: Rigor and Feasibility (Approach), scored 1-9

Criterion 3: Expertise and Resources (Investigator, Environment), to be evaluated by selecting one of the following options from a drop-down menu

    • Appropriate (no written explanation needed)
    • Identify need for additional expertise and/or resources (requires reviewer to briefly address specific gaps in expertise or resources needed to carry out the project)


Additional considerations will include: 

    • Relevance and Impact: Alignment with program objectives and potential for significant impact on healthcare equity.
    • Innovative Approach: Creativity and innovation in the proposed research and its approach to addressing healthcare disparities through AI.
    • Applicant Potential: Demonstrated potential for leadership and significant contributions to the field of AI in healthcare.

Fellowship Payments

The AIM-AHEAD Clinicians Fellowship offers each participating fellow a stipend of up to $55,475 to help defray living expenses during their training experience. The requested stipend will be commensurate with the time commitment to the Fellowship relative to the applicant's fulltime salary and cannot exceed $55,475 including other Fellowship-related expenses. This stipend can be used to pay for such expenses as faculty release time, travel to AIM-AHEAD meetings, training, conference fees and any needed supplies. Fellows are also expected to use these funds to travel to in-person AIM-AHEAD meetings. This is a fixed amount stipend. A budget is not required for application. Fellows are expected to dedicate a minimum of 25% (10 hours per week) of their time to this program. Fellows are expected to work with their Institution’s business office regarding their participation (time and effort) in the program.


Fellowship stipends will be paid as 4 quarterly equal payments for each of the four quarters of the year-long program. Upon selection, each fellow will receive a Notice of Award from UNT Health Science Center’s AIM-AHEAD office. The letter will specify the award amount and next steps.


Again, while not a requirement, clinicians with limited experience are urged to identify experienced AI mentors to support the strength of their applications before the review process. However, experienced fellows who chose not to name a mentor on their application will have the opportunity to select mentors after the award is made from the AIM AHEAD mentor pool.


Mentor Eligibility Criteria 

The mentor should be an active investigator in the proposed research area and be committed both to the career development of the candidate and to the direct supervision of the candidate’s research. S/he must document the availability of sufficient research support and facilities for high-quality research and should have a successful track record of mentoring individuals at the candidate’s career stage. Where feasible, the recruitment of women, individuals from diverse racial and ethnic groups, and individuals with disabilities as potential mentors is encouraged, given their ability to serve as role models. Full list of eligibility criteria for mentors can be found here - Link


The mentor(s) must demonstrate appropriate expertise, experience, and ability to guide the candidate in the organization, management and implementation of the proposed research. Mentors will be compensated $10,000 for their time dedicated to mentorship. Payment will be made in two equal installments of $5,000 each. The first installment of this professional fee will be paid mid-point of the program and the second at the end of the program.


Once Mentors are selected, they will receive an official email notification from UNTHSC’s AIM-AHEAD office. The notification will specify the award amount and next steps.

CLINAQ Fellowship Program Timeline




Task / Activity


Start Date

Applications Open


Application Informational Webinar


Application Deadline


Notification of Awardees


Award Execution (Begins)


Program Start Date


Onboarding Fellows and Mentor Matching


Regulatory Compliance IRB and Data Management and Data Security


Baseline Evaluation


Community Based System Dynamics Training


Mid-point Evaluation


Final Evaluation



Application Submission Guidelines, Components, and Review Process

The application process for the CLINAQ fellowship program is designed to be thorough yet straightforward, ensuring that all candidates are fairly evaluated. The application must include the following:

    • Research Proposal: Description of a research project focusing on AI in healthcare (maximum 5 pages).
    • Personal Statement: Detailing the applicant's interest in AI and its potential to impact healthcare (maximum 2 pages).
    • Curriculum Vitae: Including academic, professional, and research experiences.
    • Two Letters of Recommendation: Academic or professional references who can attest to applicant's qualifications and potential for success in the program.
    • Signed letter from home agency/institution assuring the dedicated time for the applicant's participation in the full 12-month program.
    • Signed letter from an appropriate key stakeholder setting or program confirming the stakeholder/program's agreement to host the applicant for 9 months of the program and approving the application of AI/ML methods to data within its control. Please note: The home agency/institution and key stakeholder may be the same organization for some applicants.


Application Milestones



Application Open


Application Submission Deadline


Reviews Complete


Notice of Award Dispatch


Mentor Matching


Program Start



Application and Submission Requirements

Submission Guidelines

The AIM-AHEAD Consortium utilizes the online portal InfoReady for the submission of proposal applications.

  • Step 1: Click here to login or to register as a “mentor" on AIM-AHEAD Connect (our Community Building Platform)
  • Step 2: After you login to AIM-AHEAD Connect, click here to apply for the program on the InfoReady platform*.


 * To submit your application in InfoReady, please use Chrome, Firefox, or Edge. If you're using Safari, make sure to clear your cache before logging in.

Please note both steps must be completed for consideration.

All applications must be received by June 30, 2024 —11:59 PM in applicant's time zone.

***Late applications will be returned unreviewed.


Research Project Submission Requirements

Each fellow will be expected to undertake a research project that uses AI to address a specific challenge within their respective clinical fields. Each fellow will identify a problem, develop an AI-based solution, conduct the research, analyze the results and report the findings. Fellows will have mentors to guide them through this process and collaboratively develop AI/ML models focused on health equity within their respective clinical fields.

Required Format:

    • Arial font and no smaller than 11 point; margins at least 0.5 inches (sides, top and bottom); single-spaced lines. Submit the complete application as a single word or pdf document to the application portal (provide link)
    • Enter the following profile information on AIM-AHEAD Connect:
      • Your name, organization, department (if applicable), position title, areas of interest/expertise, email address, and (optional) your profile web page
      • Your gender and race/ethnicity
    • Biosketch (limit 5 pages) in NIH ( ) or other format (Curriculum vitae of 5 pages maximum is acceptable)


Required Elements of the proposal


    • The title should describe the project in a concise, informative language.

Project Summary/Abstract (400 words maximum)

    • Provide a succinct description of the proposed work including the project’s long-term objectives, and a description of the research design and methods for the entire AI/ML project. 

Project Aims

    • Describe concisely your planned research approach, including specific aims, goals, deliverables and project timelines. (1-page limit)

Prior Studies

    • Using examples of work, by you or others, please outline how your proposed project would align with past studies. Please provide sufficient background to demonstrate project feasibility, and that your project can be completed successfully in the duration of the year.

Primary Data set

    • Describe which available AIM-AHEAD dataset (options listed in table below) will be used for the project. Describe how the selected primary dataset will contribute to answering the research topic. Applicants opting to use their own data must ensure it is appropriate for the research scope and that it meets the necessary ethical and legal standards for use in research, including patient consent and data de-identification where applicable.


    • Provide a list of references cited in the previous questions here (40 references maximum)


***A list of example research projects can be found here - Link

Notification of Award

Applicants should expect to be notified of their award status on or about August 26, 2024. Applicants who receive an award should expect to immediately begin the process of establishing a subcontract award between their home institution and the University of North Texas Health Science Center.

Awardee Resources

Available Datasets:


Data set

Brief Description

Data Allowed


Analysis platform tools

A customized subset from OCHIN Community Health Equity Database

EHR data from Underserved communities

HIPAA Limited dataset, individual-patient level data with dates and geographic indicators if needed for research

A customized subset will be created for the research question of awarded fellow from over 6 million records

AIM-AHEAD Service Workbench

Curated data from the MedStar Health EHR

EHR data from hospital system network with 31% African American patient representation

Multiple curated dataset options (further detail on website) pre-curated or custom curated de-identified EHR, Limited Dataset, Full PHI EHR dataset, Imaging, Select clinical notes, select genomics data, synthetic data

Pre-curated datasets and custom curated datasets of varying sizes


Curated from the EHR with over 5 million patient records

AIM-AHEAD Data Bridge

60+ studies from NHLBI BioData Catalyst

Selected large-scale cohorts related to heart, lung, blood and sleep disorders. Includes both prospective clinical studies and associated genomic TOPMED data.

De-identified dataset. Including individual level genomic (TOPMED full genomes) and clinical datasets.

Additional description


List of studies: 60+ studies are available to choose from

NHLBI BioData Catalyst PIC-SURE and Seven Bridges Platforms

Selected 15 Open datasets on AWS

A variety of datasets available including clinical and genomic data

Public data, and controlled access data (depends on dataset)

Selected 15 Open datasets on AWS

AIM-AHEAD Service Workbench

NIH All of Us

The All of Us Research Program is building one of the largest biomedical data resources of its kind.

The All of Us Research Hub stores health data from a diverse group of participants from across the United States.


Additional descriptions





Electronic Health Records



Biosamples Received

All of Us Researcher Workbench

The ScHARe Data Ecosystem

ScHARe is a cloud-based research collaboration platform developed by the National Institute on Minority Health and Health Disparities and the National Institute of Nursing Research

Google-hosted Public Datasets


ScHARe-hosted Public Datasets


ScHARe-hosted Project Datasets

The ScHARe Data set list

ScHARe Community Workbench



  • OCHIN - a nonprofit health care innovation center with a core mission to advance health equity, operates the most comprehensive database on primary healthcare and outcomes of traditionally underserved patients in the United States1. The OCHIN Epic EHR data warehouse aggregates electronic health record (EHR) and social determinants of health (SDH) data representing >6 million patients from 170 health systems and 1,600 clinic sites across 33 states (4.6 million patients are ‘active,’ with a visit in the last 3 years). Approved AIM-AHEAD projects can obtain access to up to 11 years of longitudinal OCHIN Epic ambulatory EHR data, which is research-ready on the PCORnet Common Data Model (CDM).
      • For additional information please visit the website: Link


  • NCATSN3C Data Enclave - The N3C Data Enclave is a secure, cloud-based research environment with a powerful analytics platform provided, which serves as the steward of N3C’s data. Since the N3C Data Enclave opened to researchers in September 2020, researchers have used the data to improve our understanding of COVID-19 and health equity, diabetes, cancer, COVID-19 medications and chronic obstructive pulmonary disease. Researchers currently are studying HIV and COVID-19 risk, mortality rates in rural populations, long COVID and much more using the N3C Data Enclave.
      • For additional information please visit the website: Link


  • ScHARe - ScHARe is a cloud-based platform for population science including social determinants of health (SDOH), and data sets designed to accelerate research in health disparities, health and healthcare delivery outcomes, and artificial intelligence (AI) bias mitigation strategies.ScHARe’s cloud-based platform contains:
      • Datasets relevant to health disparities and health care outcomes research, including social determinants of health and other social science data.
      • A data repository for the required hosting, managing, and sharing of data from NIMHD- and NINR-funded research programs.
      • Secure, collaborative workspaces and computational capabilities for researchers that facilitate access by diverse underrepresented groups and women.
      • Tools for collaboratively evaluating and mitigating biases associated with datasets and algorithms used to inform ethical and inclusive healthcare and policy decisions.
      • For additional information please visit the website: Link


  • All of Us - The All of Us Research Program is building one of the largest biomedical data resources of its kind. The All of Us Research Hub stores health data from a diverse group of participants from across the United States.
      • For additional information please visit the website: Link


  • Biodata Catalyst - Selected large-scale cohorts related to heart, lung, blood and sleep disorders. Includes both prospective clinical studies and associated genomic TOPMED data. De-identified dataset. Including individual level genomic (TOPMED full genomes) and clinical datasets.
      • For additional information please visit the website: Link


  • AIM-AHEAD Data Bridge - Curated dataset options of EHR data from underserved communities. Data available for AIM-AHEAD Data Bridge is completely de-identified and available cohorts are listed below.
      • AIM-AHEAD Data Bridge Datasets available for Research Fellowship Program:
      • Cardiometabolic correlates and maternal health
      • COVID-19 pandemic: Cardiometabolic, cancer, and behavioral health
      • Opioid use and misuse
      • Schizophrenia data
      • Voice-Assisted Personal Assistance in Heart Failure
      • Breast & Lung Cancer Images
      • Custom Dataset curated from the MedStar Health EHR - to inquire about dataset feasibility, please use the AADB intake form to schedule a data consult
      • For additional information please visit the website: Link


Computational Resources:

    • AIM-AHEAD Service Workbench - Infrastructure available for working with 1) OCHIN and 2) AWS Open Datasets. More information is available here (Link).
    • All of Us Researcher Workbench - The Researcher Workbench is a cloud-based platform where registered researchers can access Registered and Controlled Tier data. Its powerful tools support data analysis and collaboration. Integrated help and educational resources are provided through the Workbench User Support Hub. More information is available here (Link).
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