Part 1. Overview Information
Participating Organization(s) National Institutes of Health (NIH)
Components of Participating Organizations Office of Strategic Coordination (Common Fund)
Notice 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 Cancer Institute 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 PRIMED-AI: Data-to-Model Academic-Industrial Partnerships (D2M-AIP) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (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-27-012
Companion Funding Opportunity
- RFA-RM-27-011, U01 Research Project (Cooperative Agreements)
- RFA-RM-27-013, 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 Notice of Funding Opportunity (NOFO) is to catalyze the development and testing of Artificial Intelligence (AI)-enabled, image-centered, multimodal Clinical Decision Support (CDS) tools, developed in pursuance as Software as a Medical Device (SaMD). 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
- Posted Date: June 30, 2026
- Open Date (Earliest Submission Date): September 19, 2026
- Application Due 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.
- 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.
Table of Contents
- 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 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. The PRIMED-AI Program is based on the integration of clinical imaging with other types of multimodal health data that form the basis for CDS tool development and testing, which serve to enhance patient care for a wide range of health conditions. The purpose of the Data-to-Model Academic-Industrial Partnership (D2M-AIP) NOFO is to support multi-sector and multi-disciplinary research teams, including investigators from both academia and industry, to create mutually beneficial opportunities for partners in the pre-competitive development stage. D2M-AIP projects are primarily focused on the integration and harmonization of novel multiscale, multimodal data with clinical imaging data and the development and testing of truly novel AI-enabled, image-centered, multimodal CDS tools, developed in pursuance as Software as a Medical Device (SaMD). D2M-AIP projects will leverage existing resources across the partnership, such as high-performance computing capabilities and access to clinical data, to generate robust validation data and engage with regulators, positioning the technology for rapid post-award translation into a viable and impactful clinical product. Proposals should take FDA guidance for CDS tools and AI-based SaMD into account (https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software, https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device).
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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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 disease diagnosis, treatment planning and monitoring, and assessment of health and treatment outcomes; however, current developments of artificial intelligence (AI) for clinical imaging-based clinical decision support (CDS) tools typically leverage data of a single imaging modality from radiological or ophthalmological sources, while 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 new precision medicine strategies and improve patient outcomes. 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 D2M-AIP interconnections with other aspects of the PRIMED-AI Program. D2M-AIP PRIMED-AI 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 Model-to-Clinic (M2C), 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 PRIMED-AI CDS tools that function reliably in real-world healthcare settings. For the purpose of this NOFO, 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 D2M-AIP NOFO will produce a "validated AI prototype," defined as a model that has undergone sufficient technical validation to demonstrate robust performance and technical feasibility. Projects must move beyond single-site studies to ensure broad clinical applicability through comprehensive analytic validation (required) and clinical validation (where appropriate). Ultimately, the D2M-AIP NOFO aims to catalyze a new generation of AI-augmented healthcare delivery approaches that enhance diagnosis, prognosis, and/or treatment within a precision medicine framework. The value of any PRIMED-AI 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 Data-to-Model Academic-Industrial Partnership (D2M-AIP) and Model-to-Clinic (M2C) NOFOs
- D2M-AIP projects aim toward 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.
- M2C projects primarily focus on assessing clinical adoption and impact by translating a promising AI model into a clinical workflow. M2C projects must conduct clinical validation studies to evaluate real-world utility for adoption in clinical care.
Examples of responsive D2M-AIP research projects include, but are not limited to:
- Developing a novel federated AI framework to fuse ophthalmic OCT imaging with glycemic and psychiatric data for diabetic retinopathy prediction.
- A pre-competitive AI platform for integrating clinical imaging and multi-omics data that bridges the gap between scales to make predictions about treatment in patients with comorbidities and de-risk drug-induced toxicity for personalized care decisions.
- Producing reusable datasets and benchmark tasks for broader scientific use, including integration with the Genesis Mission and development of CDS tools.
- Creation and technical validation of a multiscale AI model integrating endoscopic video and digital pathology for early detection in gastrointestinal cancers.
- Development of CDS tools that perform data fusion, dynamic coupling, and dimensionality reduction in the longitudinal pairwise integration of clinical imaging, multimodal data, and outcome measures at the single patient level. CDS tools in UH3 phase leverage cohorts formed in the UG3 phase to find relationships and patterns for personalized medicine strategies, predictive AI solutions to clinical needs, explainability, and missing data.
- Development of novel digital twins CDS tools leveraging clinical imaging, genomic profiling, and real-world patient-level data (e.g., lab tests, activity and sleep data from wearables, semi-structured electronic medical record data, and patient reported outcomes). CDS tools in the UH3 phase enable personalized medicine for on-the-fly modeling of individual response trajectories to guide decisions about treatment, follow-up visits, and survivorship.
Key Requirements D2M-AIP projects supported by this NOFO will involve two distinct, milestone-driven phases of innovation research and development. In the UG3 phase, D2M-AIP award recipients will integrate comprehensive clinical imaging and multimodal data streams for AI model building and conduct initial pilot studies with a novel AI-enabled CDS tool. In the UH3 phase, award recipients will refine, further develop, and systematically validate the performance of these tools, providing evidence of their 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 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 propose the development or validation of an AI-powered, image-centered, multimodal PRIMED-AI CDS tool.
- Projects that do not define a targeted unmet clinical problem (intended use) on a clinical population (intended users) for the proposed CDS tool.
- Applications that do not include substantive academic-industrial partnership(s) as described in Section IV.
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? (link)
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 $800,000 for UH3 phases.
Award Project Period The total project period for a UG3/UH3 award may not exceed 5 years.
NIH Grants Policy 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
- 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
- Non-domestic (non-U.S.) Entities (Foreign Organizations)
Foreign Organizations/International Collaborations (list continues as in source; omitted for brevity)
Sections IV‑VIII
(details available in full announcement but omitted here for brevity)