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Find similar grantsPRECISION MEDICINE WITH ARTIFICIAL INTELLIGENCE – INTEGRATING IMAGING WITH MULTIMODAL DATA (PRIMED-AI) program: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional) is sponsored by NIH Common Fund. This opportunity supports mission-aligned projects and measurable outcomes.
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Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) | NIH Common Fund Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) The NIH Common Fund’s Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) program will combine clinical imaging with other types of health data (“multimodal data”) to develop innovative artificial intelligence (AI)-powered clinical decision support (CDS) tools to enable new personalized medicine strategies.
CDS tools are designed to help healthcare providers make informed decisions about a patient’s care by pulling information from all forms of medical data, including imaging and all other health measures. These tools help clinicians make decisions more efficiently and in a more holistic way by providing evidence-based and patient-specific recommendations.
These recommendations are based on more information than a clinician could reasonably review during a single session with a patient. Ultimately, CDS tools improve patient outcomes and quality of care. AI is revolutionizing the development of CDS tools.
Increasingly powerful AI algorithms offer new opportunities to rapidly combine vast amounts of patient data, including clinical imaging, on an individual level. These advancements have the potential to empower CDS tools to be more robust, more accurate, and even more personalized.
Although clinical imaging plays a vital role in prediction, diagnosis, treatment, and outcome assessment, weaving together imaging with other health data during the development of AI-based CDS tools remains challenging.
The PRIMED-AI program will support the development and adoption of innovative, reliable, and cost-effective AI-based CDS tools that combine clinical imaging with other types of health data to enhance care for patients with wide-ranging health conditions. Accomplishing this goal will revolutionize the delivery of personalized medicine and substantially improve quality of health for the American people.
PRIMED-AI will be centered around the following four foundational components: (1) combining imaging and non-imaging data, (2) developing AI tools, (3) implementing these tools in the clinic, and (4) building trust and coordination amongst researchers, clinicians, and patients.
The program will bring together these groups to participate in the following efforts: Directly targeting specific, unmet clinical needs by supporting the creation of sophisticated AI models to aid clinical decision making for individual patients. Accelerating translation of AI-enabled, image-based, multimodal CDS tools into practical clinical use.
Empowering academic-industrial partnerships that will develop AI-based technologies that leverage multiple data types, with the goal of enhancing the commercialization potential of novel AI algorithms. Generating a validation framework for rigorous, impartial assessment of the overall dependability of AI models developed across the PRIMED-AI ecosystem and beyond.
Supporting software tool development to address anticipated needs in key areas, as well as the development of standardized processes to support this emerging field. Enhancing communication between physicians, scientists, and patients within the PRIMED-AI space. Creating a logistics center to maximize the impact of the program through administrative and evaluative support, as well as facilitation of strategic outreach and engagement.
On April 21, 2025, The NIH Council of Councils approved the concept of the NIH Common Fund's Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program. Find out more by browsing the presentation slides , the program description , or by viewing the videocast . This page last reviewed on
According to the current listing, eligibility includes: Nonprofit organizations, public and state institutions of higher education, private institutions of higher education, independent school districts, public and Indian housing authorities, state governments, federally rec…. Confirm the full requirements in the official notice before applying.
The current listing shows $10,200,000 (Award). Verify award ceilings, matching requirements, and allowable costs in the official notice.
PRECISION MEDICINE WITH ARTIFICIAL INTELLIGENCE – INTEGRATING IMAGING WITH MULTIMODAL DATA (PRIMED-AI) program: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional) is funded by NIH Common Fund. Verify program details on the funder's official page before applying.
Yes — this listing is flagged as national in scope, so applicants across the U.S. may apply, subject to the sponsor's other eligibility criteria.
Applications go through the funder's official portal — the Apply Now link on this page goes there directly.
NOT-RM-26-004 asks the public for NIH-wide research challenges worth a 10-year, cross-institute program. There is no budget, no biosketch, no page limit, and no award. What there is: the five criteria every Common Fund investment has met since 2006, a new bullet about small-lab burden, and a fund whose FY 2026 request came in at $347.4 million — down 49.3 percent.
Read articleThe NIH Common Fund launched PRIMED-AI — Precision Medicine with AI: Integrating Imaging with Multimodal Data — as five coordinated funding opportunities (RFA-RM-27-011 through -015) that build a full pipeline from standards to clinic. Here is how the Playbook, Data-to-Model partnerships, Model-to-Clinic translation, Validation Center, and Logistics Center fit together, what each pays, who is eligible, and how to position before the October 2026 deadlines.
Read articleAvoid common NIH grant proposal mistakes including vague specific aims, weak methodology, and poor budget justification that lead to rejection.
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