PRIMED-AI: Multi-use Framework Playbooks (UG3 Clinical Trial Not Allowed)
Can you apply?
This grant is for U.S. research institutions developing AI-based tools that integrate clinical imaging with other health data. Applicants should have expertise in AI, clinical imaging, and multimodal data integration. Collaborative teams combining AI development, data harmonization, and clinical validation expertise are strongly encouraged. The grant does not support clinical trials using the UG3 activity code.
Key dates
- Jun 6, 2025 Applications open
- Jun 17, 2026 Application deadline in 16 days
- Apr 1, 2027 Award announced
- Apr 1, 2027 Project start
This grant is for U.S. research institutions developing AI-based tools that integrate clinical imaging with other health data. Applicants should have expertise in AI, clinical imaging, and multimodal data integration. Collaborative teams combining AI development, data harmonization, and clinical validation expertise are strongly encouraged. The grant does not support clinical trials using the UG3 activity code.
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’sPlaybook initiative will fund the development and initial testing of discrete projects that deliver “playbooks” of frameworks for responsible use of PRIMED-AI models, error mitigation and technical management of data and algorithms, data ontology/linkage, and preparation for regulatory approval (e.g., FDA). 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 activity code. Investigators with expertise and insights into AI-driven integration of clinical imaging with other complex and multiscale health data are encouraged to begin to consider applying for this new NOFO. In addition, collaborative investigations combining expertise in harmonization processes for appropriate development and validation of multimodal AI tools to develop AI-based technologies leveraging multimodal data will be encouraged and these investigators should also begin considering applying for this application.
Who can apply
Eligible applicants
- 501(c)(3) Public Charity
- County Government
- Private University
- Public Authority
- Public K-12 School
- Public University
- Small Business (SBA-defined)
- State Government
- Tribal Nation
- Tribal Organization
Details
This grant is for U.S. research institutions developing AI-based tools that integrate clinical imaging with other health data. Applicants should have expertise in AI, clinical imaging, and multimodal data integration. Collaborative teams combining AI development, data harmonization, and clinical validation expertise are strongly encouraged. The grant does not support clinical trials using the UG3 activity code.
How to apply
Application links
Key dates & requirements
Required documents
- SF-424 (R&R) Application
- Project Narrative and specific aims
- Budget and budget justification
- Biographical sketches of key personnel
- Letters of support from collaborating institutions
- Data management and sharing plan
- Timeline for playbook development and testing
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
-
$973,507,476
-
$383,462,829
-
$190,396,050
-
$179,743,190
-
$169,422,678
-
$167,922,818
-
$143,679,156
-
$134,358,531
-
$115,739,255
-
$91,722,927
Top States by Funding
- NC 5 awards $1,419.5M
- WA 1 awards $383.5M
- MD 2 awards $303.8M
- NY 3 awards $192.1M
- NJ 1 awards $179.7M
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 can apply for PRIMED-AI Playbooks?
Research institutions with teams experienced in AI, clinical imaging, and health data integration can apply. Collaborative projects combining data harmonization and multimodal AI development are especially encouraged.
What is the application deadline?
The fixed deadline is June 17, 2026. This is an advance notice; the formal Notice of Funding Opportunity has not been published yet.
What activities are eligible under this grant?
Eligible activities include developing playbooks for responsible AI use, error mitigation strategies, data-algorithm management, data ontology development, and regulatory preparation (FDA approval).
Are clinical trials supported?
No. Clinical trials are not permitted under the UG3 activity code for this grant.
What is the funding structure?
This is a cooperative agreement with a total funding pool of $650,000 across all awards. No cost sharing is required.
💡 Tips for applicants
- Begin building collaborative teams now. The advance notice gives you time to develop meaningful partnerships across AI, imaging, and clinical expertise before the formal application opens.
- Focus on frameworks and playbooks, not clinical validation. This grant supports development of tools and guidance for responsible AI use, not patient-centered research.
- Emphasize multimodal data integration. Proposals that demonstrate strong approaches to combining clinical imaging with other health data types will be more competitive.
- Highlight regulatory readiness. Include plans for FDA approval pathways and technical documentation standards in your project design.
- Plan for error mitigation and transparency. Strong applications will address algorithm limitations, data quality issues, and interpretability from the start.
⚠️ Common mistakes
Proposing clinical trials or patient testing; the UG3 code prohibits this. Focusing narrowly on imaging without addressing integration with other health data types. Underestimating the importance of data harmonization and ontology development in your proposal.
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