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

Sector: Power Transmission • Location: United States of America

Source: Grants.gov

Project
Forecasted

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 dat

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The project “PRIMED-AI: Translating Models to Clinic (UG3/UH3 Clinical Trial Optional)” is an infrastructure initiative in the Power Transmission sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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Description

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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