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Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional) is sponsored by NIH Common Fund. This program aims to establish the Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program to support the development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools.
These tools will integrate clinical imaging with other types of multimodal health data to enhance care for patients with a wide range of health conditions.
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Or search similar grants →The published deadline was October 2, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) (UG3/UH3, Clinical Trial Optional) is funded by NIH Common Fund. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
Past winners and funding trends for this program
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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