PRIMED-AI: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional)
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The National Institutes of Health, through the NIH Common Fund and other Institutes and Centers, is soliciting applications for the Precision Medicine with Artificial Intelligence - Integrating Imaging with Multimodal Data (PRIMED-AI) program under solicitation RFA-RM-27-013. This initiative aims to develop innovative, reliable, and cost-effective AI-based tools that integrate clinical imaging with other health data types to improve personalized medicine for patients with chronic and other health conditions. The program utilizes a biphasic UG3/UH3 activity code, focusing first on rigorous AI model building through the retrospective analysis of large, interoperable multimodal datasets to improve diagnostic accuracy, prognostic prediction, or therapeutic guidance. Projects that demonstrate strong potential and model readiness will then advance to evaluating the real-world clinical utility of the AI-driven Clinical Decision Support tool. This cooperative agreement opportunity is designed for investigators with expertise in the analysis and testing of large multimodal datasets. The notice was posted on June 10, 2025, with an application deadline of October 19, 2026. Applications may be submitted through NIH ASSIST, institutional S2S systems, or Grants.gov Workspace. The program is managed by the Office of the Director within the Department of Health and Human Services, with clinical trials listed as optional.
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Full 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.
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