Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional)

The Model-to-Clinic (M2C) for Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) funding opportunity is issued by the National Institutes of Health through the Office of Strategic Coordination within the Office of the NIH Director. This initiative is part of the NIH Common Fund, which supports high-impact, cross-cutting biomedical research programs. The opportunity is administered by the National Institute of Mental Health on behalf of NIH and is designed to accelerate the translation of artificial intelligence technologies into clinical practice. The program focuses on bridging the gap between validated AI prototypes and real-world clinical deployment, particularly for tools that integrate imaging data with other health data sources. The primary purpose of this funding opportunity is to support the development and clinical implementation of AI-enabled clinical decision support tools that combine imaging with multimodal data such as electronic health records, laboratory data, and sensor inputs. These tools must be structured as software functioning as a medical device and are expected to improve diagnostic accuracy, treatment planning, and patient outcomes. The initiative emphasizes precision medicine approaches and seeks to address significant unmet clinical needs by enabling more personalized healthcare strategies through advanced data integration. Funding is structured as a phased cooperative agreement under the UG3 and UH3 mechanisms. The UG3 phase focuses on exploratory and developmental work, including refining AI models, establishing infrastructure, and demonstrating feasibility in clinical environments. Successful completion of defined milestones allows transition to the UH3 phase, which supports advanced development, including clinical validation and deployment in healthcare workflows. The total project period may not exceed five years, and applicants must request budgets aligned with project scope, with limits on annual direct costs for each phase. Eligibility for this opportunity is broad and includes higher education institutions, nonprofit organizations, for-profit entities, and various levels of government including state, local, and tribal entities. Foreign organizations may apply, although certain restrictions apply, particularly regarding subawards. Applicants must demonstrate access to high-quality multimodal datasets and have an existing technically validated AI prototype. The program requires interdisciplinary teams with expertise in clinical care, AI development, regulatory science, and implementation within healthcare systems. Applications must be submitted electronically through Grants.gov using NIH-approved systems such as ASSIST or institutional submission tools. Applicants are required to follow detailed instructions outlined in the NIH Application Guide and must complete necessary registrations including SAM, eRA Commons, and ORCID. Applications undergo a rigorous peer review process evaluating scientific merit, feasibility, innovation, and team expertise. Additional considerations include plans for data sharing, regulatory engagement, and clinical integration. Key dates for this funding opportunity include a posted date of June 30, 2026, an earliest submission date of September 19, 2026, and a final application deadline of October 19, 2026. Applications are reviewed in early 2027, with advisory council review in May 2027 and an earliest project start date of July 2027. Late submissions are not accepted, and applicants are encouraged to submit early to allow time for corrections. Overall, the PRIMED-AI M2C program aims to catalyze the adoption of AI-based clinical decision support tools by ensuring they are robust, clinically validated, and scalable across healthcare systems. The program emphasizes collaboration, data harmonization, and real-world applicability, ultimately seeking to improve patient outcomes and healthcare efficiency through advanced AI integration.

Ensure strong clinical validation plan; demonstrate access to multimodal datasets; align with regulatory pathways early; include multidisciplinary team with clinical and AI expertise