Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional)
The summary for the Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional) grant is detailed below.
This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants.
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Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional): 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.
| Federal Grant Title: | Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional) |
| Federal Agency Name: | National Institutes of Health (HHS-NIH11) |
| Grant Categories: | Health |
| Type of Opportunity: | Discretionary |
| Funding Opportunity Number: | RFA-RM-27-013 |
| Type of Funding: | Cooperative Agreement |
| CFDA Numbers: | 93.310 |
| CFDA Descriptions: | Information not provided |
| Current Application Deadline: | October 19th, 2026 |
| Original Application Deadline: | October 19th, 2026 |
| Posted Date: | June 30th, 2026 |
| Creation Date: | June 30th, 2026 |
| Archive Date: | December 18th, 2026 |
| Total Program Funding: | |
| Maximum Federal Grant Award: | |
| Minimum Federal Grant Award: | |
| Expected Number of Awards: | 8 |
| Cost Sharing or Matching: | No |
| Last Updated: | June 30th, 2026 |
- Applicants Eligible for this Grant
- State governments - County governments - City or township governments - Special district governments - Independent school districts - Public and State controlled institutions of higher education - Native American tribal governments (Federally recognized) - Public housing authorities/Indian housing authorities - Native American tribal organizations (other than Federally recognized tribal governments) - Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education - Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education - Private institutions of higher education - For-profit organizations other than small businesses - Small businesses - Others (see text field entitled "Additional Information on Eligibility" for clarification.)
- Additional Information on Eligibility
- Refer to Section III. Eligibility Information in the NOFO for additional information on eligibility.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.
- Grant Announcement Contact
- PRIMED-AI Program
[email protected]
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