Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 25, 2025
Date Accepted: Nov 24, 2025
NIH-Funded Artificial Intelligence and Machine Learning Research, 2019-2023: A Cross-Sectional Study
ABSTRACT
Background:
Artificial intelligence (AI) and machine learning (ML) technologies have begun to spread into and revolutionize the practice of medicine. The National Institutes of Health (NIH) began tracking “Machine Learning and Artificial Intelligence” as its own funding category in fiscal year (FY) 2019. With proposed federal budget cuts threatening NIH’s ability to support biomedical innovation, understanding recent trends in AI/ML funding and the composition of principal investigators (PIs) is critical for informing policy.
Objective:
We aimed to examine trends in AI/ML research funded by the National Institutes of Health (NIH) and to characterize the population of funded PIs.
Methods:
We analyzed data from NIH RePORTER between October 1, 2018, and September 30, 2023 (FY 2019-2023) that was reported under the “Machine Learning and Artificial Intelligence” funding category. We aggregated number of funded projects and principal investigators (PIs), by FY year. Additionally, we randomly sampled 25% of funded PIs and performed an internet search to collect data on educational background, (e.g. MD, PhD), research setting (e.g. academic, industry) and clinical specialty (as appropriate). Data were summarized with descriptive statistics.
Results:
Active projects increased from 1,229 to 3,449 and total funding increased from $0.6 to $2.3 billion between FY 2019 and 2023. In our random sample of PIs, the most common educational background was PhD-only (70%). Amongst clinical PIs, psychiatry (11%) and cardiology (10%) had the largest proportion of funded grants, while PIs in surgical subspecialties (13%) and neurology (13%) had the highest funding shares ($).
Conclusions:
Funding for ML/AI research increased by 771% between FY 2017 and 2023, outpacing the overall NIH budget increase of 43%. However, this growth has disproportionately favored PIs with PhD-only backgrounds and certain clinical specialties. These findings underscore the need for strategic investments that promote a well-represented, interdisciplinary Al/ML research workforce across a broad range of research settings. Clinical Trial: N/A
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.