Request for Information

The Common Fund issued a Request for Information (RFI, NOT-RM-24-011) to pinpoint key challenges and opportunities in developing reliable, cost-efficient, accessible, ethical, and sustainable AI algorithms for precision medicine. 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.

Summary of RFI Responses

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 was 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 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.

Full Report of RFI Responses

A complete summary of RFI responses is available here.