Call for Proposals

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

Fellowship Program - Cohort 2

Overview and Purpose

The AIM-AHEAD Clinicians Leading Ingenuity IN AI Quality (CLINAQ) Fellowship Program is a one-year initiative (September 2, 2025 – August 31, 2026) designed to empower clinicians by integrating Artificial Intelligence and Machine Learning (AI/ML) expertise directly into clinical practice. This interdisciplinary training seeks to cultivate a workforce skilled in the evaluation, development and responsible application of robust and generalizable AI technologies. Participants will engage in comprehensive virtual and in-person didactic sessions, webinars, and interactive workshops, equipping them to effectively leverage AI innovations within clinical settings. By enhancing clinicians' proficiency in AI/ML, the program aims to advance medical research, improve patient outcomes, and promote the creation of AI models that are scientifically rigorous, broadly applicable and aligned with the needs of all patient populations.

Background

NIH’s AIM-AHEAD is establishing mutually beneficial, coordinated, and trusted partnerships to empower researchers and communities across the United States in the development of Artificial Intelligence and Machine Learning (AI/ML) models and improve the capabilities of this emerging technology, beginning with electronic health record (EHR) and extending to other lifestyle data to improve health for all Americans. The rapid increase in the volume of data generated through EHR and other biomedical research presents exciting opportunities for developing 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, there is a lack of comprehensive and high-quality artificial intelligence (AI)-ready data and a shortage of a pipeline of talented researchers in industry and academia to harness the potential of AI/ML to advance biomedical research and the practice of medicine. This program aims to build AI talents and technology to improve the health of all Americans. The program will bring AI tools to impact patients and support hospitals that otherwise would not have had the resources or bandwidth to investigate advances in AI and machine learning (ML).

NIH is committed to leveraging the potential of AI/ML to accelerate the pace of biomedical innovation. Tackling the complex drivers of health outcomes require an innovative and transdisciplinary framework that transcends scientific and organizational silos. Mutually beneficial and trusted partnerships can be established to empower researchers and communities across the United States in AI/ML modelling, application and improve the capabilities of data curation and this emerging technology.

The AIM-AHEAD program aims to establish mutually beneficial, coordinated, and trusted partnerships to empower researchers and communities across the United States in the development of artificial intelligence and machine learning (AI/ML) models and improve the capabilities of this emerging technology, beginning with the use of electronic health record (EHR) and extending to other lifestyle data to improve health of all Americans.

The AIM-AHEAD Coordinating Center (A-CC) is building a consortium of institutions and organizations from all stakeholder groups (academic institutions, community organizations, private businesses, non-profits, and healthcare organizations) across the nation. A-CC focuses on coordination, assessment, planning, and capacity building to enhance the use of artificial intelligence (AI) and machine learning (ML) in research among the consortium institutions and organizations; and to build and sustain trusted relationships between the consortium and groups impacted by health problems.

The A-CC is comprised of four cores to support AIM-AHEAD investigators:

The Leadership/Administrative Core leads the A-CC, recruits and coordinates consortium members, project management, partnerships, stakeholder engagement, and outreach to develop AI/ML talented researchers in health research, and establishes trusted relationships with key stakeholders to enhance the volume and quality of data used in AI/ML research. The Leadership/Administrative Core operates as a Pass-Through Entity for the FAIR-MED program.

The Data Science Training Core assesses, develops and implements a robust data science training curriculum and workforce development resources in AI/ML.

The Data and Research Core addresses research priorities and needs by linking and preparing multiple sources and types of research data, forming an inclusive basis for AI/ML use cases that illuminates strategies and approaches to address health problems. To accomplish its mission, the Data and Research Core facilitates the extraction and transformation of data from electronic health records (EHR) and data on lifestyle contributors to health for research use.

The Infrastructure Core assesses data, computing and software infrastructure models, tools, resources, data science policies, and AI/ML computing models to facilitate AI/ML and health research; and establishes pilot data and analysis environments to accelerate overall A-CC aims.

The Consortium Members and the communities they serve will vary in terms of their research interests, preferences around data sharing and data governance, and training needs. A one-size-fits-all approach to infrastructure, training, research directions, or engagement is unlikely to achieve the overall goals of the AIM-AHEAD program. Dedicated efforts are needed, therefore, in each of these areas.

The Consortium Members and the communities they serve will vary in terms of their research interests, preferences around data sharing and data governance, and training needs. A one-size-fits-all approach to infrastructure, training, research directions, or engagement is unlikely to achieve the overall goals of the AIM-AHEAD program. Dedicated efforts are needed, therefore, in each of these areas.

Program Objectives

Each fellow will be expected to conduct a research project that uses AI to address a specific challenge impacting the fellow’s clinical field. 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 improving healthcare within their respective clinical fields. Fellows with minimal AI/ML experience will not be excluded, but will need to identify an internal AI/ML mentor/collaborator in their application.

Each fellow will be expected to produce at least one peer-reviewed publication or abstract based on the fellow’s research project, fellowship experience, observations, changes in practice behavior, and implementation of AI/ML in the clinical practice or clinical research settings.

Fellows will also be responsible for co-creating a group peer-to-peer presentation and tailoring it to disseminate their newly acquired familiarity with AI/ML to a variety of clinical practice settings and specialties, in a manner that minimizes any disruption to clinical responsibilities (e.g., leading peer-to-peer sessions as part of a hospital Grand Rounds series in the year following graduation from the fellowship).

Expected Outcomes:

• The establishment of a supportive network of clinicians and AI experts dedicated to promoting representation of all communities in clinical care and research.
• A comprehensive understanding of current AI development in healthcare and its potential impact on the health of our communities.
• Actionable recommendations and strategies for broadening clinician participation in the development of AI to address chronic disease conditions.
• An appreciable increase in the number of clinicians who are skilled in integrating AI algorithm development for clinical solutions.

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 conduct a research project that addresses a specific challenge in healthcare using AI. The fellow will identify a problem, develop an AI-based solution, conduct the research, and analyze the results. Fellows will have mentors to guide them through this process.
• Fellows will be expected to report their progress on their research projects at the AIM-AHEAD annual conference (to be held July or August 2026).
• 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 conference presentations, seminars, and publications. These presentations are 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.
• Being part of the fellowship program also involves contributing to the community of fellows by sharing knowledge, providing support, and participating in peer mentoring when possible.
• The fellows will be required to report regularly on the progress of their research and projects. This requirement includes meeting the milestones set at the beginning of the fellowship and participating in evaluations to assess the impact of their work.

Awardee Resources

Available Datasets:

• OCHIN – OCHIN is a nonprofit leader in healthcare innovation and operates the most comprehensive database on healthcare and outcomes of primary care patients in the United States, including 1.3 million rural residents. The OCHIN Epic EHR data warehouse aggregates electronic health record (EHR) and SDoH 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.
• NCATS N3C 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, 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
• ScHARe - ScHARe is a cloud-based platform for population science including SDoH and data sets designed to accelerate research in health and healthcare delivery outcomes, and artificial intelligence (AI) strategies. ScHARe’s cloud-based platform contains:
  • Datasets relevant to health disparities and healthcare outcomes research, including SDoH and other social science data.
  • A data repository for the required hosting, managing, and sharing of data from NIMHD- and NINR-funded research programs.
  • For additional information please visit the website
• 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 participants representing various groups, and communities, from across the United States.
  • For additional information please visit the website
• Biodata Catalyst - Selected large-scale cohorts related to heart, lung, blood and sleep disorders. This de-identified dataset contains both prospective clinical studies and associated genomic TOPMED data including individual level genomic (TOPMED full genomes) and clinical datasets.
  • For additional information please visit the website
• AIM-AHEAD Data Bridge - Curated dataset options of EHR data from an extensive network of clinical facilities in the mid-Atlantic region. Data available for AIM-AHEAD Data Bridge is completely de-identified. The following cohorts are available:
  • • 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 

Structured Courses and Trainings:

Fellows and Mentors will follow the curricular activities using the AIM-AHEAD Connect platform. This platform will serve as the central hub throughout the fellowship. Here, fellows and mentors will find all necessary information regarding due dates, activities and program requirements. Each month, a range of synchronous and asynchronous activities, discussions, workshops, and project updates will be available to participants. The fellowship team will assist participants in managing key milestones, tracking their goals, and staying on top of essential tasks to achieve success in the program.

Curricular activities are categorized into four primary groups:

• Community-based system dynamics workshop
• Clinical leadership/team science training
• CLINAQ AI Workshop Series
• CLINAQ AI Curriculum

The following is an overview of these activities and their structure within the fellowship timeline. All activities are integrated into the AIM-AHEAD Connect Hub as goals, tasks, and milestones.

• Community Based System Dynamics Training (In-person)

Community based system dynamics (CBSD) is a participatory practice for centering communities in the process of developing a shared understanding of complex problems from a dynamic feedback perspective.  CBSD training will offer substantial benefits to CLINAQ fellows 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 drivers of healthcare challenges and develop AI solutions that are robust, and tailored to the needs of all communities. This participatory approach enhances the design and implementation of AI technologies and also ensures that these innovations effectively address real-world healthcare complexities. 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 health for all communities. This community-based system science approach will be utilized by fellows and applied to design AI/ML interventions for clinical settings.

• 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, and AI governance, alongside workshops aimed at fostering innovation and personal resilience. Training also emphasizes the importance of 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) lead transformative changes in clinical care, 2) manage broadly representative teams, and 3) promote fair and responsible AI practices, positioning them as future leaders in the intersection of AI and healthcare.

• CLINAQ 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. 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 challenges of AI/ML in healthcare, receive guidance on refining their methodologies, and build valuable professional relationships that extend beyond the fellowship duration.

• CLINAQ AI Curriculum

A detailed Curriculum Structure provides a month-by-month breakdown of the program covering knowledge and skills to implement Responsible AI practices that promote quality health care for all. The curriculum is organized in four key levels: Technical Foundations in AI, Responsible AI in Practice, Human-Centered AI, and AI Governance.

The four curriculum content-levels are:

1. Technical Foundations in AI: Fellows will learn the technical foundations of AI to develop and implement AI projects, and effectively interact with AI developers, collaborators and vendors.
2. Responsible AI in Practice: Fellows will apply Responsible AI practices for application of AI at the point-of-care, including patient engagement, outpatient care, inpatient care, emergency care, mental health, diagnostics, clinical operations, quality and patient safety, population health, public health, and health policy.
3. Human-Centered AI: Fellows will design, develop, implement, and monitor AI solutions in clinical practice, ensuring they are human-centered and address health questions for everyone.
4. AI Governance: Fellows will learn and apply regulations and governance frameworks to ensure the objective application of AI in healthcare.

Through these levels, participants will develop core competencies in Ingenuity, and Leadership, ensuring they are well-prepared to lead AI solutions in the health care sector benefiting all communities.

AIM-AHEAD North Stars

North Star 1: Develop a representative AI/ML workforce with broad participation.

North Star 2: Increase knowledge, awareness and national-scale community engagement and empowerment in AI/ML. 

North Star 3: Use AI/ML to improve behavioral health, cardiometabolic health and cancer outcomes for all.

North Star 4: Build community capacity and infrastructure in AI/ML to address community-centric health needs and challenges. 

The CLINAQ fellowship program aligns to all AIM-AHEAD North Stars by 1) broadening the AI/ML workforce through specialized clinical training; 2) increasing public literacy and engagement via community education efforts by fellows; 3) developing AI/ML solutions to address specific focus areas like behavioral health, cancer, and cardiovascular and metabolic health; and 4) building capacity in clinics for responsible AI deployment. With a curriculum grounded in participatory methods, the fellowship prepares clinicians to apply AI to improve health outcomes for all.

Eligibility

Important Notice

Consistent with NIH practice and applicable law, AIM-AHEAD programs may not use the race, ethnicity, or sex of prospective program participants or faculty as an eligibility or selection criteria. The race, ethnicity, or sex of candidates will not be considered by AIM-AHEAD in the application review process or when making funding decisions.

Eligible Organizations

Higher Education Institutions

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

Nonprofits

• Nonprofits with 501(c)(3) IRS Status
• Nonprofits without 501(c)(3) IRS Status
• Community-Based Organizations
• Tribal health and/or human service organizations or Tribally derived institutions (e.g. Urban Indian Health Organizations, Tribal Epidemiology Centers)

For-Profit Businesses/Organizations

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

The primary applicant organization must be a domestic institution/organization located in the United States and its territories. Before applying, these organizations must be registered with System for Award Management (SAM; see https://sam.gov/content/home) and must maintain active SAM registration throughout the award period (please see below for required registrations).

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.

Required Registrations for Applicant Organizations

Applicant organizations must complete and maintain the following registrations as described in the SF 424 (R&R) Application Guide to be eligible to apply for or receive an award. All registrations must be completed before submitting the application. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. The NIH Policy on Late Submission of Grant Applications states that failure to complete registrations in advance of a due date is not a valid reason for a late submission.

System of Award Management (SAM): Applicants must complete and maintain an active registration, which requires renewal at least annually. The renewal process may require as much time as the initial registration. SAM registration includes the assignment of a Commercial and Government Entity (CAGE) Code for domestic organizations that have not already been assigned a CAGE Code. Federally recognized tribes and their derivatives are exempt from this requirement.

Unique Entity Identifier (UEI): A UEI is issued as part of the SAM.gov registration process. The same UEI must be used for all registrations, as well as the grant application.

eRA Commons: Once the UEI is issued, organizations can register with eRA Commons in tandem with completing their Grants.gov registration. All registrations must be in place at the time of submission. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application.

Grants.gov registration: Applicants must have an active SAM registration in order to complete the Grants.gov registration.

The sponsoring institution/organization must:

• Be able to obtain an IRB determination (even if the determination is Not Human Subjects / Exempt Research). Private IRB reviews are allowable
• Provide institutional signoff on Data Use Agreements / Data Sharing Agreements 

Eligible Applicants

To be eligible for this program, the applicant must fulfill the following criteria and requirements:

• For the purposes of this program, clinician-scientists include individuals with an MD, DO, DDS/DMD, MD/PhD, DO/PhD, PA, DNP/FNP, or nurses with research doctoral degrees who devote the majority of their time to clinical care. Additional professional designations may be considered on a case-by-case basis. If you need further clarification regarding your eligibility, please submit a helpdesk ticket: CLINAQ Program HelpDesk
• Candidates must be U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals.

U.S. Citizen: Any individual who is a citizen of the United States by law, birth, or naturalization

Permanent Resident: A status given to United States immigrants/non-citizens who can legally reside in the United States in perpetuity

Non-Citizen National: 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

• Accepted candidates must be able to submit a W-9 tax form.
• Temporary visa holders (FI, JI, HI, etc.) are not eligible.

Application Submission and Review Deadlines

Application Milestones

Fellowship Payments

The AIM-AHEAD Clinicians Fellowship offers participating fellows 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 full-time salary, and cannot exceed $55,475 including other Fellowship-related expenses. The stipend is a fixed amount; a budget is not required. This stipend can be used to pay for such expenses as faculty release time, travel to AIM-AHEAD annual meeting, training, conference fees and any needed supplies. Fellows are also expected to use these funds to travel to the AIM-AHEAD meeting (2-days, July/Aug 2026). 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 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.

AIM-AHEAD Mentors

Each awarded fellow will receive mentorship from an experienced, skilled individual who will guide the fellow in applying AI/ML methods and leadership skills to navigate the organizational cultures and climates in order to strengthen their clinical practice. Each Fellow will be assigned one AIM-AHEAD Mentor chosen from a pool of AIM-AHEAD Mentor applicants. The AIM-AHEAD Mentor Pool Application will open from August 4, 2025, to August 29, 2025. AIM-AHEAD Mentors will be selected and assigned during the fellow’s onboarding process. Given the distinctive design of the clinician fellowship program, our plan is to recruit former AIM-AHEAD Research and Leadership Fellows who are also clinicians to serve as mentors for the inaugural cohort of clinician fellows.

CLINAQ Mentors

In addition to their AIM-AHEAD mentor, fellows will have the option of having a CLINAQ mentor (Ideally from their Institution). CLINAQ mentors should be clinicians with active AI model development experience who can provide tailored support and guidance in the application AI/ML within clinical contexts. Applicants with minimal AI/ML experience are encouraged to identify a mentor/collaborator and include them throughout the Fellowship Application Process.

Mentor Eligibility Criteria

Mentors should be active investigators in the area of the proposed research and be committed both to the career development of the candidate and to the direct supervision of the candidate’s research. Mentors must document the availability of sufficient research support and facilities for high-quality research. Candidates are encouraged to identify more than one mentor if a mentoring team is deemed advantageous for providing expert advice in all aspects of research career development. In such cases, one individual must be identified as the primary mentor who will coordinate the candidate’s research. The mentor, or a member of the mentoring team, should have a successful track record of mentoring individuals at the candidate’s career stage. The mentor selection process will prioritize qualifications, including research productivity, alignment with the fellow’s project, and the ability to provide meaningful career development support. Full list of eligibility criteria for mentors can be found here.

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

Application Submission Deadline

Applications should be submitted by June 23, 2025, using the link to upload the following required documents:

• Research Proposal: Description of a research project focusing on AI in healthcare (max 5 pages).
• Personal Statement: Detailing the applicant's interest in AI and its potential to impact healthcare (max 2 pages).
• Biosketch (or Curriculum Vitae): Including academic, professional, and research experiences and accomplishments.
• Two Letters of Recommendation:
  • 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.

Research Proposal Submission Requirements

The applicant must propose a research project that uses AI to address a specific challenge or problem in the applicant’s clinical field.  During the fellowship, each Fellow will conduct a research project to test an AI-based solution to the identified challenge/problem, analyze the results and report the findings.  The Mentor(s) will guide the Fellow through this process and collaborate on the development of AI/ML models focused on health disparities within the Fellow’s clinical field

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 InfoReady application portal.

• 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
• Biosketch (5 pages limit) in NIH (https://grants.nih.gov/grants/forms/biosketch.htm ) or other format. A curriculum vitae of 5 pages maximum is acceptable.

Required Elements of the Research

Title 

• 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 central hypothesis, rationale and long-term objectives, and a summary of the research design and methods for the entire 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 demonstrate how your proposed project would align with past studies, and identify the critical challenge or gap in knowledge your project will address. Please provide sufficient background to demonstrate project feasibility and show that your project can be completed successfully in the duration of the year.

Cohorts

• Describe the cohorts of patients, conditions, and/or other sets of data that will be needed.

Primary Data set

• Describe how the selected primary dataset will contribute to answering the research topic.

References

• Provide a list of references cited in the proposal here (40 references maximum).

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

A Review Committee comprised of AIM-AHEAD Consortium members will use the following scientific criteria to evaluate proposals.  In assigning priority scores, reviewers will apply to the following criteria 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 - https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html

• The proposal outlines a high-impact research topic and demonstrates creativity and innovation in its application of AI to address healthcare challenges in the research area.
• The proposal outlines how the selected dataset(s) will contribute to effectively addressing the research topic. Applicants are required to verify the presence of these variables within their chosen dataset(s).
• The proposal addresses an important scientific question that can be addressed within the 1-year timeframe using the AIM-AHEAD ecosystem and the available data resources.
• The proposal is aligned with program objectives and has the potential to impact disparities in healthcare delivery.
• The applicant has demonstrated the necessary background and capabilities to accomplish the proposed work.
• The applicant demonstrates the potential for leadership and significant contributions to the field of AI in medicine.
• The application describes approaches and/or tools that will benefit the functionality of the AIM-AHEAD ecosystem.
• The applicant has a willingness to engage and collaborate with the AIM-AHEAD community, contribute to documentation and training resources, welcome and empower new users, and help foster a broadly representative community.
• Other potential items for evaluation: Research environment and resources; Institutional support; Collaborators (if any)

Applicants should expect to be notified of their award status on August 4, 2025. 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.

More details to come

FAQs

Please refer to the Frequently Asked Questions document before creating a help desk ticket.

Help Desk

Please direct questions regarding the Course Development Awards to the contacts listed below or create a Help Desk ticket: CLINAQ Program HelpDesk

Program Director: Herman Taylor