Strategic Planning

PRIMED-AI Concept Planning

The NIH is conducting planning activities to inform a potential Common Fund research program called Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI). This program concept is aimed at harnessing advancements in artificial intelligence (AI) technologies to enable integration of clinical imaging data with a variety of other health data to support the clinical decision-making process. Such a program would transform disease prevention, detection, diagnosis, and treatment, ultimately improving patient outcomes.

PRIMED-AI Concept Public Input

The Common Fund issued a request for information (RFI) to identify high priority challenges and opportunities in developing trustworthy, cost-effective, accessible, ethical, and sustainable precision medicine AI algorithms that integrate medical images with other patient-related data to support clinical decision making. Community input was requested on:  

• Imaging and related multimodal data integration approaches, 
• Developing AI-based clinical decision support tools that leverage clinical imaging and multimodal data integration, 
• Demonstrating clinical utility of multimodal algorithms for precision medicine.   

Responses were collected through September 23, 2024. 

Executive Summary of the PRIMED-AI Concept Public Input

The goal of the PRIMED-AI program concept is to determine how to use AI to bring together medical imaging with multiple, diverse types of health data (or multimodal data) in a way that supports precision medicine. The approach to the concept is informed by extensive input from the public and the research community, through responses to a Request for Information (RFI) and several informational calls. These inputs underscored that standardized imaging protocols, high-quality data curation, and robust ethical considerations to ensure the reliability, reproducibility, and fairness of AI-driven healthcare tools would be necessary for PRIMED-AI to achieve its vision. Collaborative efforts involving academia, industry, and healthcare providers, alongside innovative AI methods, are essential for developing robust AI models that can be used in a variety of situations. The following core concepts will guide any future PRIMED-AI funding announcements:

1. Standardization and Data Integration: Emphasizing standardized imaging protocols, such as those needed for MRI sequences, and meticulous metadata retention to improve the reliability of AI models.
2. AI-Based Clinical Decision Support Tools: Addressing the need to harmonize machine learning platforms and address bias and privacy concerns through advanced AI techniques.
3. Demonstrating Clinical Utility: Highlighting the importance of diverse datasets and partnerships with Electronic Health Record (EHR) vendors and others to measure the impact of AI tools on patient outcomes.
4. Ethical Considerations: Focusing on data privacy, bias mitigation, and interdisciplinary collaboration to ensure the equitable and ethical deployment of multimodal AI tools in precision medicine.
5. Innovative Methods: Adopting cutting-edge AI approaches such as federated learning  to enhance data privacy, model transparency, and overall AI robustness.
    

 A complete summary is available here.