Part 1. Overview Information

Participating Organization(s)

National Institutes of Health (NIH)

Components of Participating Organizations

Office of Strategic Coordination (Common Fund)

This Notice of Funding (NOFO) is developed as a Common Fund initiative (https://commonfund.nih.gov/) through the Office of the NIH Director, Office of Strategic Coordination (https://commonfund.nih.gov/). All NIH Institutes and Centers participate in Common Fund initiatives. The NOFO will be administered by the National Institute of Mental Health on behalf of the NIH.

Note: Not all NIH Institutes, Centers, and Offices (ICOs) participate in Announcements. Applicants should carefully note which ICOs participate in this announcement and view their respective areas of research interest at the ICO-Specific Scientific Interests website. ICOs that do not participate in this announcement will not consider applications for funding.

Funding Opportunity Title

Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional)

Activity Code

UG3/UH3 Exploratory/Developmental Phased Award Cooperative Agreement

Announcement Type

New

Related Notices

Check for any recent Notices of NIH Policy Changes that may impact application requirements.

Funding Opportunity Number (FON)

RFA-RM-27-013

Companion Funding Opportunity

RFA-RM-27-011, U01 Research Project (Cooperative Agreements)
RFA-RM-27-012, UG3/UH3 Phase 1 Exploratory/Developmental Cooperative Agreement/Exploratory/Developmental Cooperative Agreement Phase II
RFA-RM-27-014, U54 Specialized Center (Cooperative Agreements)
RFA-RM-27-015, U24 Resource-Related Research Project (Cooperative Agreements)

Number of Applications

See Part 2, Section III. 3. Additional Information on Eligibility.

Assistance Listing Number(s)

93.310

Funding Opportunity Purpose

The overarching goal of this notice of funding opportunity (NOFO) and its companion opportunities is to establish the Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program to support development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools. PRIMED-AI CDS tools are based on the integration of clinical imaging with other types of multimodal health data to enhance care for patients with a wide range of health conditions. The PRIMED-AI Program seeks to catalyze the adoption of AI-based CDS tools into clinical workflows to enable novel personalized medicine strategies that address significant health challenges.

The purpose of this NOFO is to catalyze the translation of Artificial Intelligence (AI)-enabled, image-centered, multimodal CDS tools, developed as Software as a Medical Device, from training and testing of prototypes towards clinical applications that address unmet health challenges in precision medicine. These projects are expected to have high potential for demonstrable, positive impact on patient outcomes and/or healthcare processes.

Funding Opportunity Goal(s)

The Office of Strategic Coordination (Common Fund) supports research and other projects that will accelerate fundamental biomedical discovery and translation of that knowledge into effective prevention strategies and new treatments.

Key Dates

Application Due Dates	Review and Award Cycles	AIDS - New/Renewal/Resubmission/Revision (as allowed)	Scientific Merit Review	Advisory Council Review	Earliest Start Date
October 19, 2026	Not Applicable	Not Applicable	March 2027	May 2027	July 2027

All applications are due by 5:00 PM local time of applicant organization. Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

No late applications will be accepted for this Notice of Funding Opportunity (NOFO).

Due Dates for E.O. 12372

Not Applicable

Expiration Date

October 20, 2026

Required Application Instructions

It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide, except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts).

Conformance to all requirements (both in the Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the Application Guide, follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

Use the NIH ASSIST system to prepare, submit and track your application online.
Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.

Part 1. Overview Information
Key Dates
Part 2. Full Text of Announcement
Section I. Notice of Funding Opportunity Description
Section II. Award Information
Section III. Eligibility Information
Section IV. Application and Submission Information
Section V. Application Review Information
Section VI. Award Administration Information
Section VII. Agency Contacts
Section VIII. Other Information

Part 2. Full Text of Announcement

Section I. Notice of Funding Opportunity Description

Purpose

The Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) NOFOs seek to spur on the development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools with the potential for transformational impact. Overall, the PRIMED-AI Program catalyzes the integration of clinical imaging with other types of multimodal health data that informs CDS tool development and testing in clinical workflows, which serves to enhance patient care for a wide range of health conditions.

The Model-to-Clinic (M2C) NOFO uniquely seeks projects that take CDS tools developed as Software as a Medical Device (SaMD), from validated prototypes to clinical applications.

M2C projects are structured as phased innovation awards (UG3/UH3) designed to advance multimodal AI-based CDS tools through progressive stages of development and validation.

UG3 Phase (Exploratory/Developmental): This initial phase focuses on establishing the technical and operational foundation for clinical translation. Projects will develop and refine AI models, regulatory preparations, clinical implementation infrastructure, deployment strategies, and demonstrate preliminary feasibility in target clinical settings. Milestones must be achieved before transitioning to the UH3 phase.

UH3 Phase (Advanced Development): Following successful completion of the UG3 phase, the UH3 phase emphasizes CDS model clinical implementation and validation. Projects will deploy CDS tools in clinical workflows; conduct prospective clinical studies to evaluate performance; demonstrate potential for impact and integration with existing health IT systems; and generate evidence for broader adoption and sustainability. Projects must demonstrate measurable potential for clinical impact and pathway(s) to sustained use beyond the award period.

The combined UG3/UH3 award period cannot exceed 5 years total. Transition from UG3 to UH3 requires achieving specified milestones and NIH approval.

M2C projects are expected to have high potential for demonstrable, positive impact on patient outcomes and/or healthcare processes.

Key Terms used in PRIMED-AI Program

Clinical decision support (CDS) tool: A type of software, computational model or digital system that is incorporated into clinical workflows to assist in determining a course of action related to patient care.
Clinical imaging: Any FDA-approved imaging modality used in patient care, including radiologic (e.g., radiographic, computed tomographic, magnetic resonance, molecular, radionuclide imaging), ophthalmologic (e.g., Optical Coherence Tomography), endoscopic, and dermatologic imaging, and video. Clinical imaging of human participants is intended to be the anchor data type that multimodal data are integrated with in the PRIMED-AI Program, which will form the basis for AI algorithm development and testing of CDS tools.
DICOM standard: Digital Imaging and Communications in Medicine (DICOM) standard, the most widely used by the community to address interoperability challenge, is strongly encouraged but not required. Inclusion of non-DICOM standard clinical imaging must include a plan to develop standards in conjunction with the PRIMED-AI community if none currently exist.
Harmonization: The process of bringing together data from different sources and ensuring that it is consistent, comparable, and compatible. This involves standardizing data formats, structures, and definitions so that data from various sources can be integrated and analyzed together effectively.
Interoperability: The ability for AI models and associated data and metadata to be understood and work across different AI platforms and have the potential to be used consistently across different health systems.
Multimodal data (MMD): Representing different types of data and information from multiple sources that may include multiple clinical imaging modalities and non-imaging health data (e.g., electronic health records, EEG, EKG, laboratory test results (-omics), wearable sensor data, medical reports). Multiscale data are encouraged; however, microscopy-based imaging of biospecimens ex vivo (e.g., digital pathology) cannot represent the sole imaging data type. Although non-human imaging and/or MMD data may have assisted in development of an AI-model, overt representation and reliance on data derived from non-human sources for CDS tool development, testing, and validation will be given low programmatic priority.
Playbook: A collection of actionable guidelines, standardized protocols, and/or standardized operating procedures for the reliable and effective development and deployment of multimodal clinical decision support tools. The Playbook is a collection of frameworks.
Precision Medicine: Sometimes called personalized medicine or individualized medicine, refers to a healthcare approach that uses information based on a patient's individual characteristics such as health measures, genotype, phenotype, environment, and lifestyle information to guide, tailor, and optimize decisions related to their medical care and management.
PRIMED-AI Consortium: The consortium constitutes members of PRIMED-AI excluding NIH program staff. PRIMED-AI Program is an umbrella term encompassing the consortium, NIH staff, and overall programmatic objectives.
Uncertainty Quantification: Measuring or quantifying the impact of uncertainties in complex systems, including quantifying the confidence in outcomes predicted by multimodal AI models.
Validation: Validation exists on a continuum in the PRIMED-AI Program. Analytical or technical validation is based on the evaluation of algorithmic performance and the ability of a multimodal AI model to make accurate predictions. Initially, a model or algorithm can meet expected performance on retrospective and/or entirely new clinical datasets within the confines of a specific hospital or healthcare system. It is useful locally (internally) but is not yet applicable (generalizable) to the wider real-world population. Subsequently, for clinical validation, a model or algorithm can be tested (externally) on new wider real-world population datasets to predict a meaningful outcome and meet regulatory criteria for the claimed use case. The PRIMED-AI Program anticipates validation of projects along this continuum as outlined in the NOFOs.
Verification: The process by which data integrity and construction of models is assessed for appropriateness within the context of use or intended purpose.

Background

Clinical imaging plays a pivotal role in diagnosis, treatment, and assessment of health outcomes; however, it is often utilized in isolation from other types of data during the development of artificial intelligence (AI)-based clinical decision support (CDS) tools. Current AI applications typically leverage a single imaging modality from radiological or ophthalmological sources. Because health is shaped by a variety of interconnected factors–clinical, biological, genetic, environmental and social–the Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program seeks to integrate clinical imaging with relevant, complementary multimodal data (MMD). The overarching goal of the PRIMED-AI Program is to catalyze the development and adoption of innovative AI-based CDS tools into clinical workflows. The PRIMED-AI Program initiatives collectively aim to tackle complex clinical challenges by fostering cross-disciplinary collaboration to create innovative, reliable, cost-effective, and sustainable AI solutions that enable precision medicine strategies.

Prior to applying, applicants are encouraged to check the PRIMED-AI Program website for updates to relevant FAQs and informational webinars, and are also encouraged to read all companion NOFOs to ensure they are aware of the goals and responsibilities of all PRIMED-AI award recipients, including methods PRIMED-AI intends to utilize to address error mitigation and technical management. Familiarity with the companion NOFOs may better inform proposed M2C interconnections with other aspects of the PRIMED-AI Program.

M2C CDS tools aim to enhance diagnostic accuracy, optimize treatment, improve prognostic prediction, and streamline workflows for clinical decision-making through processing and integration of complex, multimodal data streams to ultimately improve patient outcomes and/or lower patient care costs. This initiative and its companion Data-to-Model Academic-Industrial Partnership (D2M-AIP) directly address a critical translational gap in AI research–the gap between promising AI prototypes developed in controlled research environments and robustly validated, clinically deployable CDS tools that function reliably in real-world healthcare settings. For the purpose of these NOFOs, applicants must demonstrate sufficient access to verified, comprehensive, and complex multimodal datasets as well as integration and harmonization of these data to address a target clinical application. Applicants to the M2C must have a technically validated AI prototype, defined as a model that has undergone technical validation at a minimum of a single site, or unimodally, or in retrospective data, or distinctly validated model types that need to be integrated (e.g., hybrid models that combine different types of data or logic such as machine learning-based with rule-based, or logic-based, neuromorphic based, or dynamical system theories).

During the PRIMED-AI project period, M2C projects must perform comprehensive technical and clinical validation, beyond single-site studies to ensure broad clinical applicability. Ultimately, the M2C NOFO aims to deliver a set of valuable and effective CDS tools able to perform appropriately in real-world clinical settings within a precision medicine framework.

The value of any CDS tool developed under this program is contingent upon its relevance, reliability, and broad clinical adoption to improve patient care and reduce patient care costs.

Distinction Between Model-to-Clinic (M2C) and Data-to-Model Academic Industrial Partnerships (D2M-AIP) NOFOs

The PRIMED-AI Program includes two parallel NOFOs designed with distinct primary foci along the data-to-model-to-clinic continuum. To help applicants select the appropriate funding opportunity, the essential differences are summarized here:

M2C projects primarily focus on assessing clinical adoption and impact by translating a promising AI model(s) into a clinical workflow as CDS tools. M2C projects must conduct clinical validation studies to evaluate real-world utility for adoption in clinical care. M2C projects begin with a promising AI model and aim to assess and validate its clinical performance, utility, and impact in real-world healthcare settings. They should also address challenges related to adopting AI-assisted CDS tools in clinical workflows, including associated costs and feasibility.
D2M-AIP projects aim towards commercialization and are led by an academic-industrial partnership that primarily focuses on novel data integration and/or new AI model development. Projects proposed under D2M-AIP exist in the pre-competitive space and will emphasize technical validation and performance testing, without requiring clinical validation studies within the award period. The goal is to develop and de-risk novel AI technologies through collaboration, preparing them for future commercialization, adoption, and use of a CDS tool(s).

Key Requirements

M2C projects supported by this NOFO will involve two distinct, milestone-driven phases of innovation research and development. In the UG3 phase, M2C award recipients will make the CDS tools deployable and generalizable across multiple sites, integrating comprehensive, large-scale, clinical imaging and multimodal datasets. In the UH3 phase, applicants apply these tools in the clinical setting with the potential for transformative impact on real-world challenges in precision medicine. Applications must be built upon the following five pillars (further specified in Section IV):

Data Quality and Governance for Model Development
Clinically Grounded AI Technology
Pathway to Implementation and Adoption
Integrated Multidisciplinary Team
Error Mitigation and Technical Management

Applications Not Responsive to this NOFO. To be considered responsive, applications must align with the central goal of the PRIMED-AI Program.

The following types of applications will be considered non-responsive and will not be reviewed:

Projects primarily focused on basic research in AI/ML methodology without a clear and significant translational goal towards a specific clinical problem.
Projects that do not focus on the translation of a CDS tool that uses clinical imaging as the anchor data type, integrated with other multimodal data. Clinical imaging must be the modality that anchors the model. Digital pathology cannot represent the imaging anchor data type but may serve as a form of multimodal data.
Projects where the primary focus is on a mechanistic biological research question and the AI technology and methods are already well-established, adapted, optimized, and validated for that context of use.
Applications proposing Phase III clinical trials as the primary scope of work. (Note: While this NOFO is "Clinical Trial Optional," large-scale, confirmatory Phase III trials are outside the scope).
Projects that do not define a targeted unmet clinical problem (intended use) on a clinical population (intended users) for the proposed CDS tool.
Projects without an existing prototype of an AI-powered, image-centered, multimodal CDS tool.

Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.

See Section VIII. Other Information for award authorities and regulations.

Section II. Award Information

Funding Instrument

Cooperative Agreement: A financial assistance mechanism used when there will be substantial Federal scientific or programmatic involvement. Substantial involvement means that, after award, NIH scientific or program staff will assist, guide, coordinate, or participate in project activities. See Section VI.2 for additional information about the substantial involvement for this NOFO.

Application Types Allowed

New

The OER Glossary and the How to Apply Application Guide provide details on these application types. Only those application types listed here are allowed for this NOFO.

Clinical Trial?

Optional: Accepting applications that either propose or do not propose clinical trial(s).

Need help determining whether you are doing a clinical trial?

Funds Available and Anticipated Number of Awards

The NIH Common Fund intends to commit funds for approximately 6-8 UG3/UH3 awards. The number of awards is contingent upon NIH appropriations and the submission of a sufficient number of meritorious applications.

Award Budget

Applicants should request a budget appropriate for the proposed scope of work, not to exceed $450,000 in direct costs per year for UG3 and $1,000,000 in direct costs for UH3 phases.

Award Project Period

The total project period for a UG3/UH3 award may not exceed 5 years.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. Eligible Applicants

Eligible Organizations

Higher Education Institutions - Includes all types
- Public/State Controlled Institutions of Higher Education
- Private Institutions of Higher Education
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)
Local 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)

Federal Governments

U.S. Federal Government Agencies (e.g., NIH Intramural Research Program, DOE National Laboratories) may participate as partners but are not eligible to apply as the primary applicant institution
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
Non-domestic (non-U.S.) Entities (Foreign Organizations)

Foreign Organizations/International Collaborations

Non-domestic (non-U.S.) Entities (Foreign Organizations) are eligible to apply.
Non-domestic (non-U.S.) components of U.S. Organizations are eligible to apply.
Foreign components, as defined in the NIH Grants Policy Statement, are allowed.

NIH will no longer issue awards (i.e., new, renewal, or non-competing continuation) to domestic or foreign entities that involve foreign subawards/subcontracts. All NIH-funded research involving foreign subawards/subcontracts must be submitted in response to a NOFO that is specifically designated for funded international collaborations. See NIH Grants Policy Statement 16.8 Collaborative International Research Awards.

Applications involving foreign subawards/subcontracts submitted in response to this NOFO will be deemed noncompliant and will not be considered for funding. This policy applies to all monetary international collaborations resulting in foreign subawards/subcontracts, however, it does not preclude unfunded international collaborations or foreign components, funding for foreign consultants, or procurement of unique equipment or supplies from foreign vendors.

Required Registrations

Applicant Organizations Applicant organizations must complete and maintain the following registrations as described in the How to Apply- Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. Failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications for additional information.

System for 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 which have not already been assigned a CAGE Code. Foreign organizations must obtain a NATO Commercial and Government Entity (NCAGE) Code (in lieu of a CAGE code) in order to register in SAM.
- 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 on the grant application.
eRA Commons - Once the unique organization identifier is established, organizations can register with eRA Commons in tandem with completion