NIH Funding for Artificial Intelligence in Surgical Specialties: Allocation and Alignment with US Disease Burden
BACKGROUND: National Institutes of Health (NIH) investment in artificial intelligence (AI) within surgical specialties is rapidly expanding, yet whether funding aligns with disease burden or reflects structural disparities remains unclear. STUDY DESIGN: Cross-sectional analysis of NIH RePORTER (1993-2024) identified 1,657 AI grants across eight surgical specialties. Multivariable regression of 976 grants to 379 investigators (2021-2024) evaluated associations between funding and 2021 disability-adjusted life years (DALYs), adjusting for region, investigator sex, and degree. Primary endpoints were total funding and grant number relative to disease burden; secondary analyses assessed regional and investigator variation. RESULTS: NIH AI funding was not associated with disease burden after adjustment (funding: IRR 1.00, 95% CI 0.99-1.01, p>0.9; grant number: IRR 0.93, 95% CI 0.87-0.99, p=0.033). Funding per DALY varied widely across disease categories without proportional allocation to higher-burden conditions. Regional disparities were evident: compared with the Midwest, the South received more grants (IRR 1.46, 95% CI 1.10-1.94) but lower per-grant funding (IRR 0.79, 95% CI 0.65-0.94). Female investigators received fewer grants than male investigators (IRR 0.69, 95% CI 0.57-0.84), although per-award funding was similar. CONCLUSIONS: NIH funding for AI research in surgical specialties is not aligned with disease burden and instead reflects regional and investigator-level variation. These findings identify structural disparities in funding allocation and establish a benchmark for policy efforts to better align AI research investment with population health needs.Précis: Analysis of 1,657 NIH grants for AI research in surgical specialties found funding was not associated with US disease burden, and correlated with regional and investigator characteristics. We highlight the need for further research into factors driving allocation in this emerging field.
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NIH Funding for Artificial Intelligence in Surgical Specialties: Allocation and Alignment with US Disease Burden
OpenAlex · Artificial Intelligence in Healthcare and Education · 2026
Abstract
BACKGROUND National Institutes of Health (NIH) investment in artificial intelligence (AI) within surgical specialties is rapidly expanding, yet whether funding aligns with disease burden or reflects structural disparities remains unclear. STUDY DESIGN Cross-sectional analysis of NIH RePORTER (1993-2024) identified 1,657 AI grants across eight surgical specialties. Multivariable regression of 976 grants to 379 investigators (2021-2024) evaluated associations between funding and 2021 disability-adjusted life years (DALYs), adjusting for region, investigator sex, and degree. Primary endpoints were total funding and grant number relative to disease burden; secondary analyses assessed regional and investigator variation. RESULTS NIH AI funding was not associated with disease burden after adjustment (funding: IRR 1.00, 95% CI 0.99-1.01, p>0.9; grant number: IRR 0.93, 95% CI 0.87-0.99, p=0.033). Funding per DALY varied widely across disease categories without proportional allocation to higher-burden conditions. Regional disparities were evident: compared with the Midwest, the South received more grants (IRR 1.46, 95% CI 1.10-1.94) but lower per-grant funding (IRR 0.79, 95% CI 0.65-0.94). Female investigators received fewer grants than male investigators (IRR 0.69, 95% CI 0.57-0.84), although per-award funding was similar. CONCLUSIONS NIH funding for AI research in surgical specialties is not aligned with disease burden and instead reflects regional and investigator-level variation. These findings identify structural disparities in funding allocation and establish a benchmark for policy efforts to better align AI research investment with population health needs.Précis: Analysis of 1,657 NIH grants for AI research in surgical specialties found funding was not associated with US disease burden, and correlated with regional and investigator characteristics. We highlight the need for further research into factors driving allocation in this emerging field.