State and Diffusion of National Institutes of Health Funding of AI in Radiology.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Duke University, Durham, NC, USA. [email protected].
- Department of Radiology, Duke University, Durham, NC, USA.
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Abstract
Artificial intelligence research has profound implications for the future of radiology, making it essential to understand funding patterns and diffusion rate from the National Institutes of Health (NIH), historically the leading source of biomedical research funding in the United States. Recent changes in federal funding further necessitate understanding the trends and focus areas for future comparison and strategic decisions by researchers, institutions, and policymakers adapting to the evolving funding landscape. This retrospective study searched and analyzed active NIH-funded projects as of January 2025 and temporally over the last decade (2015-2024) using the NIH RePORTER and ExPORTER databases. An automated large language model pipeline was employed for thematic extraction and categorization of active projects. Diffusion rate analyses were performed to examine the progression of funding distribution across institutes. Descriptive statistics were provided for grant types, administering institutes, principal investigator details, organizations, geography, and research topics. Among active grants focused on AI in radiology, the National Cancer Institute led in total projects (188; $117.0 M), while the National Heart, Lung, and Blood Institute had the greatest funding ($167.3 M). The most common grant type for AI in radiology was R01 (547 projects; $326.1 M), followed by R21 (85 projects; $24.2 M) and U01 (51 projects; $65.6 M). Funding was concentrated in major academic institutions. Over the years, annual funding grew approximately 13.7-fold from $46.4 M (FY2015) to $633.5 M (FY2024), and integration of AI projects into radiology research increased approximately eightfold (from 3.9% to 30.4%). AI diffusion demonstrated exponential growth (Compound Annual Growth Rate, CAGR 25.5%, R<sup>2</sup> = 0.97) with a doubling time of 2.94 years. At 30.4% penetration, the field has entered the Early Majority phase, approaching a critical 50% inflection point projected for 2028-2030. Topic analysis of awarded NIH grants on AI in radiology revealed high frequency of deep learning applications, magnetic resonance imaging, and neurological applications followed by oncology.