Landscape Analysis

The PRIMED-AI landscape analysis examined and mapped out current applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) within biomedical and clinical research, specifically focusing on the integration of medical imaging and non-imaging multimodal data. This study assessed ongoing developments and emerging trends to evaluate their potential in driving innovation in patient care and enhancing health outcomes. The insights gained from this analysis contributed to shaping the PRIMED-AI program concept.

Summary of the PRIMED-AI Landscape Analysis

Interest, investment, and expertise exist, but are siloed.

• There is interest in AI, clinical imaging, and precision medicine projects that aligns with the PRIMED-AI concept, but efforts are scattered and often siloed. The scope of NIH-supported projects in these areas has often focused on a single data type in isolation, such as radiological scans, retinal photographs, or pathology images (1).
• The number of related projects supported by the NIH has remained largely unchanged (2).
• The NIH currently has no coordinated investment in this area; additional funding could enable significant strategic progress on precision medicine by providing paths for interdisciplinary team science to overcome current limitations in growth and scope.

AI/ML infrastructure and capacity are rapidly building.

• Due to the complexity of human health data, AI/ML is essential for processing large, diverse datasets to identify patterns and make advanced predictions that unlock potential to directly benefit both individual patients and their caregivers (3).
• The integration of AI with multimodal data analysis in medical imaging has the potential to significantly enhance precision medicine (4).
• A paradigm shift toward AI-supported analysis is needed to promote integration of medical imaging with multimodal health data in enabling novel precision medicine strategies.

Investment in the PRIMED-AI concept is timely.

• The AI medical imaging market is growing rapidly, driven by technological advancements and an increasing demand for clinical imaging (5). As a result, investments in AI initiatives like those proposed for PRIMED-AI are highly advantageous and well-positioned for impact and success.
• The global market for AI deployment in healthcare is ripe. Internationally, AI approaches applied to medical imaging are advancing through a variety of initiatives and investments (6).
• Existing regulatory frameworks are not yet fully equipped to handle the complexities of AI. PRIMED-AI would be uniquely positioned to provide continuity through partnerships across diverse communities and influence the regulatory space (3).

There are many potential federal and non-federal partners that could enhance the PRIMED-AI concept.

• There are multiple NIH and ARPA-H programs that align with the proposed timeline of PRIMED-AI and could be leveraged for infrastructure, tools, and data to support PRIMED-AI awardees. These programs could complement each other and support an NIH-wide initiative on AI that is “bigger than the sum of its parts.”
• The implementation and adoption of PRIMED-AI deliverables would be improved through collaborative partnerships such as with FDA, FNIH, AHRQ, and ARPA-H.
• Private partners should be evaluated for potential engagement in academic-industrial partnerships with awardees, transition partners for PRIMED-AI developments, and community stakeholders to inform the rapidly developing AI space. 

NIH is well-positioned to lead this potential initiative.

• While the NIH is broadly investing in research related to AI/ML, clinical imaging, and precision medicine, a coordinated programmatic effort that catalyzes an intersection of these three topics does not currently exist.
• The PRIMED-AI concept is synergistic, disease agnostic, and would advance the missions of multiple NIH Institutes, Centers, and Offices, 18 of which are represented in the PRIMED-AI working group.
• The PRIMED-AI concept addresses obstacles for the precision medicine field that are uniquely suited to team science solutions at the NIH-wide level.

References for Landscape Analysis

1. Analysis of internal administrative data. ref1
2. Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC). ref2
3. Labkoff, S., Oladimeji, B., Kannry, J., Solomonides, A., Leftwich, R., Koski, E., Joseph, A. L., Lopez-Gonzalez, M., Fleisher, L. A., Nolen, K., Dutta, S., Levy, D. R., Price, A., Barr, P. J., Hron, J. D., Lin, B., Srivastava, G., Pastor, N., Luque, U. S., Bui, T. T. T., Singh R., Williams T., Weiner M. G., Naumann T., Sittig D. F., Jackson G. P., Quintana, Y. (2024). Toward a responsible future: recommendations for AI-enabled clinical decision support. Journal of the American Medical Informatics Association: JAMIA, 31(11), 2730–2739. ref3
4. Poalelungi, D. G., Musat, C. L., Fulga, A., Neagu, M., Neagu, A. I., Piraianu, A. I., & Fulga, I. (2023). Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. Journal of Personalized Medicine, 13(8), 1214. ref4
5. ARPA-H Investor Catalyst Hub. (2024). Medical Imaging Data Marketplace Survey Report. Advanced Research Projects Agency for Health (ARPA-H). ref5
6. Contreras, B. (2023). AI in medical imaging market expected to increase to $14.2 billion by 2032. Managed Healthcare Executive. ref6