The Academy for Radiology and Biomedical Imaging Research (ARBIR)'s advocacy efforts intensified this week ahead of the Trump administration's fiscal year (FY) 2027 budget request.
As part of its 2026 Medical Imaging Research Hill Day activities, ARBIR hosted a roundtable discussion March 25 highlighting imaging's substantial stake in biomedical research, and uncertainties in NIH research funding vehicles.
Radiologist and Academy for Radiology and Biomedical Imaging Research President Pamela Woodard, MD, emphasized collaboration in preparation for AI-driven advances in imaging science.Photo courtesy of Pamela Woodard/ARBIR
This year the Academy is focused on two critical areas, according to President Pamela Woodard, MD, who also chairs of the department of radiology at Washington University School of Medicine in St. Louis, MO. One is leveraging the academy's collaborative strategic approach to prepare for AI-driven advances in imaging science, and the second is strengthening the pipeline by supporting individuals at every stage of their careers.
"Both topics speak to our future, how we innovate, who will lead that innovation in research, and practical applications," Woodard said to open the day.
NIH funding goal
The ARBIR and Ad Hoc Group for Medical Research have recommended the U.S. government allocate $51.303 billion for the National Institutes of Health (NIH) for fiscal year FY 2027, a $4.087 billion or 8.7% increase over the enacted funding level for NIH in FY 2026.
Total NIH funding has remained strong, although FY 2025 saw significant shifts in how awards were distributed, according to the ARBIR.
A March 12 NIH report confirmed that while the number of awards for all extramural research fell 6.2% in 2025, the total amount of funding grew 0.4%. FY 2025 saw continued growth in applications submitted for research project grants (RPGs), but about 30% of applications do not succeed to funding. The same was true for R01-equivalent grants.
NIH has announced its intentions to move to "unified funding strategy." In anticipation of the U.S. House and Senate hammering out their funding bills in the months ahead, ARBIR is drawing attention to medical imaging researchers and the weight of medical imaging data in NIH-funded biomedical research.
All the data show there's been NIH-wide growth in technology development and impact, noted Bruce Tromberg, PhD, director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the NIH.
Bruce Tromberg, PhD, is director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the NIH. Photo courtesy of Bruce Tromberg/ARBIR
An analysis of 80,000 grants in 2024 found 24% of awards to be imaging or imaging related, according to Tromberg.
"That's an astounding number but actually not surprising because you need [imaging] as an essential tool," he said. "People are not developing technologies but are actually dependent upon and using these technologies to drive their discoveries. So that makes us a really essential driver of all the work that's going on at the NIH."
Translational gap
However, it is time to bridge a critical gap, Tromberg noted.
"All of us are frustrated with the gap between what patients receive on a routine basis, and where the research community is, and how we bridge that gap," he noted. "This will get us closer to our goal of diagnostic-led precision medicine."
Multiyear NIH research funding is a sensitive point, Tromberg added, highlighting that language in the current budget bill is unclear in that area. However, he also acknowledged that there has been a 22% increase in both biomedical imaging and biomedical engineering funding over last five years.
Importantly, in schools of engineering around the U.S., human health has become a top priority, Tromberg explained.
"Medical schools are recognizing that engineering is actually important in technology development," Tromberg said.
Radiologists and engineers together are needed more than ever, according to Tromberg, who pointed out three academic centers for their engineering and medicine dual-degree programs -- University of Illinois Urbana-Champaign, Arizona State University, and Texas A&M University.
Imaging forward
Radiology has entered a new era of convergence, noted keynote speaker Elias Zerhouni, MD, a renowned radiologist, former chair of the department of radiology at Johns Hopkins University School of Medicine, and author of the book "Disease Knows No Politics."
"The key for imaging moving forward is to have a wide view of what imaging is."
With biological processes better understood at the cellular and molecular level, Zerhouni cautioned against a silo mentality.
"The key for imaging moving forward is to have a wide view of what imaging is," said Zerhouni, who previously led the NIH and now serves as president and chair of the board of Opko Health, a biopharmaceutical and diagnostics company.
Radiologist Elias Zerhouni, MDPhoto courtesy of Elias Zerhouni/ARBIR
"What is going to be different is that the radiologist will be like the control tower," Zerhouni predicted. Data is the absolute basis of the success of deep learning and large language models and now agentic AI, which is AI that builds on top of large language models, he added.
"The data layer, that's 90% of the effort ... it's not something that you can really master by having nice software in your department," he explained. "If you look at the analysis of the brain, there are companies now that have accumulated enough MRI of the brain to differentiate diseases in a way that we couldn't do before."
Accumulating the most data cannot be done by any single institution or any single group, it will have to be collaborative, Zerhouni noted.
Coming soon
In a glimpse into the path forward, the NIH recently announced a $150 million initiative to develop, validate, and standardize research tools toward sophisticated and relevant models of disease. It prioritizes human-based research to reduce the use of animal models.
What this means is that research teams across the U.S. will lead projects designed to produce more predictive disease models, according to the NIH. Technology development centers will facilitate new-approach methodology (NAM) development in the areas of gynecological disorders, cardiac disease, neurological disorders, rare diseases, to name a few.
NIH plans for a NAM Data Hub and Coordinating Center (NDHCC) to facilitate data sharing and standards development, while a validation and qualification network (VQN) will leverage a public-private partnership with industry and regulatory expertise necessary to implement reliable, marketable NAMs.
In another initiative, the NIH has also launched the Precision Medicine with Artificial Intelligence – Integrating Imaging with Multimodal Data (PRIMED-AI) program. The NIH Common Fund, with other NIH Institutes and Centers, intends to publish a notice of funding opportunity, under the UG3/UH3 activity code, to solicit applications for the program.
PRIMED-AI's Translating Model to Clinic initiative will initially focus on rigorous AI model building, according to the NIH. NIH has encouraged investigators with expertise and insights into analysis and testing of large, interoperable multimodal datasets to consider applying; the estimated application due date is June 17, 2026.
The NIH said projects demonstrating strong potential and model readiness will advance to evaluating the real-world clinical utility of AI-driven clinical decision support tools.
