PRIMED-AI: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional)

The National Institutes of Health (NIH), through its Common Fund and in collaboration with other NIH Institutes and Centers (ICs), has announced a forthcoming Notice of Funding Opportunity (NOFO) titled "PRIMED-AI: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional)." This initiative, part of the broader Precision Medicine with Artificial Intelligence - Integrating Imaging with Multimodal Data (PRIMED-AI) program, seeks to bridge the gap between artificial intelligence model development and real-world clinical implementation. The NIH Common Fund supports bold scientific programs that cut across various ICs, enabling high-impact research that may not be feasible through standard funding channels. Through PRIMED-AI, NIH aims to enhance diagnostic, prognostic, and therapeutic capabilities by integrating diverse health data types with advanced AI tools. This forecasted opportunity focuses on developing innovative, robust, and cost-efficient AI-driven tools for Clinical Decision Support (CDS) by leveraging clinical imaging and multimodal datasets. The Translating Model to Clinic initiative uses a biphasic UG3/UH3 cooperative agreement mechanism. The initial UG3 phase emphasizes retrospective analysis and model validation using large, interoperable health datasets. Projects that demonstrate readiness and potential impact will transition to the UH3 phase, focusing on evaluating clinical utility in real-world settings. This approach is designed to ensure only the most promising AI models reach the implementation stage, thereby improving clinical care quality and efficiency in addressing chronic and other medical conditions. The program encourages applications from investigators with demonstrated expertise in managing and analyzing large-scale, multimodal biomedical data. While applications are not currently being accepted, NIH is providing early notice to allow time for the formation of robust collaborative teams and the design of responsive, well-integrated research proposals. The award mechanism provides structured funding through two distinct phases, fostering both initial model development and subsequent clinical translation. The total estimated program funding for this opportunity is $10.2 million, although specific award ceilings and floors have not been published. Eligible applicants include a broad spectrum of institutions and organizations, such as public and private institutions of higher education, independent school districts, state and county governments, public housing authorities, nonprofits with 501(c)(3) status, tribal organizations, and both small and large for-profit entities. The program does not require cost sharing or matching, making it accessible to a wide range of applicants focused on advancing clinical decision support through AI. NIH underscores the importance of collaborative research and translational readiness in proposal evaluation. According to the current forecast, the estimated post date for the NOFO is April 17, 2026, with applications due by June 17, 2026. Awards are expected to be issued by March 1, 2027, which will also serve as the project start date. This timeline allows applicants ample time to prepare their proposals and assemble interdisciplinary research teams. As a forecasted opportunity, the information is subject to change, and interested applicants are encouraged to monitor official NIH and Grants.gov pages for updates. Contact for this program is Dr. Sahana N. Kukke of the NIH Office of the Director, Common Fund. She can be reached at 301-402-3756 or via email at ODPRIMED-AI@od.nih.gov. As additional documents and application forms are not yet available, potential applicants are advised to begin internal planning efforts and consider this NOFO as part of their strategic grant development timeline. The NOFO reflects NIH's ongoing commitment to harnessing cutting-edge technology in service of precision medicine and public health outcomes.