Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional)
🏛 National Institutes of Health (HHS-NIH11)
✓ Free, no account · Source: Grants.gov · Last verified Jul 28, 2026
Can you apply?
This grant is for organizations developing AI-based clinical tools that integrate imaging with other health data. Eligible applicants include research institutions, medical centers, and organizations with expertise in AI model development and clinical validation. Projects must focus on chronic or other health conditions where current diagnostic, prognostic, or therapeutic methods are insufficient. The program operates in two phases: initial AI model development using retrospective multimodal datasets, followed by real-world clinical utility evaluation for selected projects.
Geographic scope is national. Applicants must have access to large, interoperable datasets and clinical partnership capabilities. No cost-sharing is required. Organizations must demonstrate capacity for rigorous AI validation and clinical implementation.
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Key dates
- Jun 30, 2026 Applications open
- Oct 19, 2026 Application deadline in 82 days
- Mar 1, 2027 Award announced
- Mar 1, 2027 Project start
Program description
The NIH Common Fund, with other NIH Institutes and Centers (ICs), intends to publish a Notice of Funding Opportunity (NOFO) to solicit applications for the Precision Medicine with Artificial Intelligence – Integrating Imaging with Multimodal Data (PRIMED-AI) program, which seeks to develop innovative, reliable, and cost-effective AI-based tools that integrate clinical imaging with other health data types to enhance personalized medicine for patients with chronic and other health conditions. The program’s Translating Model to Clinic initiative will use a biphasic mechanism to initially focus on rigorous AI model building through the retrospective analysis and testing of large, interoperable multimodal datasets, laying a robust foundation for improved diagnostic accuracy, prognostic prediction, or therapeutic guidance where current methods fall short. Projects demonstrating strong potential and model readiness will advance to evaluating the real-world clinical utility of the AI-driven Clinical Decision Support (CDS) tool. Applications are not being solicited at this time. Notice is being provided to allow potential applicants sufficient time to develop meaningful collaborations and responsive projects. This NOFO will utilize the UG3/UH3 activity code. Investigators with expertise and insights into analysis and testing of large, interoperable multimodal datasets are encouraged to begin to consider applying for this new NOFO.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- SF-424 (R&R) Federal Application Form
- Project Narrative/Research Plan
- Budget and Budget Justification
- Curriculum Vitae (key personnel)
- Letters of Support (clinical partners, data providers)
- Data Access/Use Agreements
- Institutional Commitment Letters
Program contact
- 👤 Sahana N. Kukke, PhD Office of the Director/NIH Common Fund, DPCPSI, OD
- 📧 ODPRIMED-AI@od.nih.gov
- 📞 301-402-3756
Funding track record
Recent awards under CFDA 93.310 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
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$973,507,476
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$383,462,829
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$190,396,050
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$179,737,926
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$169,422,678
-
$167,922,818
-
$147,947,250
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$143,679,156
-
$115,739,255
-
$103,665,364
Top States by Funding
- CA 3 awards $1,196.2M
- NC 4 awards $446.1M
- WA 1 awards $383.5M
- MD 2 awards $317.4M
- NY 4 awards $261.2M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 93.310). How funding has trended year over year.
| 2024 | $1,174,839,078 | |
| 2025 | $1,062,277,534 | |
| 2026 est. | $28,100,048 |
FAQ
Who is eligible to apply for PRIMED-AI?
Research institutions, medical centers, and organizations with strong AI expertise and clinical partnerships. Applicants need access to large multimodal datasets and clinical validation infrastructure.
What are the two phases of this program?
Phase 1 (UG3) focuses on rigorous AI model building using retrospective datasets. Phase 2 (UH3) evaluates real-world clinical utility of the AI tool for selected Phase 1 awardees.
What types of health conditions are in scope?
Projects can target any chronic or other health conditions where current diagnostic, prognostic, or therapeutic methods are insufficient. Imaging-based approaches with integrated multimodal data are prioritized.
Is cost-sharing required?
No, this program does not require cost-sharing or matching funds from applicants.
When is the application deadline?
The fixed deadline is June 17, 2026. The NOFO has not yet been published; this is advance notice to allow time for collaboration development.
💡 Tips for applicants
- Begin building collaborative partnerships now with clinical sites, imaging experts, and data access providers. Strong partnerships directly impact competitiveness.
- Identify and secure access to large, high-quality multimodal datasets before submitting. Dataset quality and interoperability are critical evaluation factors.
- Demonstrate clear clinical gaps where your AI approach provides meaningful improvement over current methods. Articulate the unmet clinical need explicitly.
- Plan realistic Phase 1 milestones focused on rigorous model validation using retrospective data. Phase 2 advancement depends on demonstrating model readiness and clinical promise.
- Engage biostatisticians and clinical validation experts early. Rigorous evaluation design and transparent performance reporting are essential for competitive applications.
⚠️ Common mistakes
Applications often lack rigorous dataset validation and interoperability documentation. Poor clinical partnership articulation or insufficient access agreements weaken competitiveness. Underdeveloped Phase 1-to-Phase 2 transition plans and unclear metrics for model readiness cause rejections.
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