Overview

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.

Background

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.

Purpose/Objectives

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. 1. 1. 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. 1. 1. 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

Eligibility

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.

• Applicants must be U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals.
  • US Citizen definition: any individual who is a citizen of the United States by law, birth, or naturalization (https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=42-USC-630966247-802284531&term_occur=1&term_src=title:42:chapter:99:section:9102)
  • Permanent Residents definition: a status given to United States immigrants/non-citizen who can legally live in the United States in perpetuity (https://www.law.cornell.edu/wex/lawful_permanent_resident_(lpr))
  • Non-Citizen Nationals definition: a person born in an outlying possession of the United States on or after the date of formal acquisition of the United States at birth (https://www.law.cornell.edu/uscode/text/8/1408)

***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)

Governments

• • 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

Other

• • 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 Statement, are not allowed.

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 http://www.nsf.gov/statistics/showpub.cfm?TopID=2&SubID=27) 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 (https://www.govinfo.gov/content/pkg/FR-1997-10-30/html/97-28653.htm ).

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, https://www.nsf.gov/statistics/2017/nsf17310/static/data/tab7-5.pdf.

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

1. 1. Were or currently are homeless, as defined by the McKinney-Vento Homeless Assistance Act (Definition: https://nche.ed.gov/mckinney-vento/);
   2. Were or currently are in the foster care system, as defined by the Administration for Children and Families (Definition: https://www.acf.hhs.gov/cb/focus-areas/foster-care);
   3. Were eligible for the Federal Free and Reduced Lunch Program for two or more years (Definition: https://www.fns.usda.gov/school-meals/income-eligibility-guidelines);
   4. Have/had no parents or legal guardians who completed a bachelor’s degree (see https://nces.ed.gov/pubs2018/2018009.pdf);
   5. Were or currently are eligible for Federal Pell grants (Definition: https://www2.ed.gov/programs/fpg/eligibility.html) ;
   6. Received support from the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) as a parent or child (Definition: https://www.fns.usda.gov/wic/wic-eligibility-requirements).
   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 (https://data.hrsa.gov/tools/rural-health), 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 https://nces.ed.gov/programs/coe/indicator_tva.asp), and are subsequently less likely to be represented in biomedical research. For background see Department of Education data at:

https://nces.ed.gov/; https://nces.ed.gov/programs/coe/indicator_tva.asp; https://www2.ed.gov/rschstat/research/pubs/advancing-diversity-inclusion.pdf.

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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008902/).

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 https://www.nsf.gov/statistics/2017/nsf17310/ , 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).

AIM-AHEAD Mentors

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.