PRIMED-AI: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional)
This NIH grant will fund development and clinical testing of AI tools that combine imaging and other health data to improve personalized care for chronic and other conditions.
Funder: National Institutes of Health
Due Dates (Anticipated): June 2027 (full application, projected) | June 2026 (application, projected)
Funding Amounts: Estimated total program funding: $10,200,000; typical UG3/UH3 cooperative agreement; award size and duration to be specified in NOFO.
Summary: Supports development and clinical translation of AI-based tools integrating imaging with multimodal data to advance precision medicine for chronic and other health conditions.
Key Information: This is a forecasted opportunity; applications are not yet being solicited.
Description
The NIH Common Fund, in collaboration with other NIH Institutes and Centers, anticipates releasing a Notice of Funding Opportunity (NOFO) for the Precision Medicine with Artificial Intelligence - Integrating Imaging with Multimodal Data (PRIMED-AI) program. The program aims to develop innovative, reliable, and cost-effective AI-based tools that integrate clinical imaging with other health data types (e.g., genomics, EHR, labs) to enhance personalized medicine, particularly for chronic and complex health conditions.
The Translating Model to Clinic (M2C) initiative employs a biphasic (UG3/UH3) cooperative agreement mechanism:
- Phase 1 (UG3): Focuses on rigorous AI model building through retrospective analysis and testing of large, interoperable multimodal datasets to improve diagnostic accuracy, prognostic prediction, or therapeutic guidance.
- Phase 2 (UH3): For projects demonstrating strong potential and model readiness, advances to evaluating the real-world clinical utility of the AI-driven Clinical Decision Support (CDS) tool in clinical settings.
This opportunity is intended for investigators with expertise in the analysis and testing of large, interoperable multimodal datasets who are interested in translating AI tools into clinical practice.
