ArticleJournal of imaging informatics in medicine2026
State and Diffusion of National Institutes of Health Funding of AI in Radiology.
Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Authors and funding
9 authors.
Funding
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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
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Identifiers
41673229What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.