NIH Funding for Artificial Intelligence in Surgical Specialties: Allocation and Alignment with US Disease Burden

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.

Cross-sectional analysis of NIH Research Portfolio Online Reporting Tools Expenditures and Results (1993 to 2024) identified 1,657 AI grants across 8 surgical specialties. Multivariable regression of 976 grants to 379 investigators (2021 to 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.

NIH AI funding was not associated with disease burden after adjustment (funding: incidence rate ratio [IRR] 1.00, 95% CI 0.99 to 1.01, p > 0.9; grant number: IRR 0.93, 95% CI 0.87 to 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 to 1.94) but lower per-grant funding (IRR 0.79, 95% CI 0.65 to 0.94). Female investigators received fewer grants than male investigators (IRR 0.69, 95% CI 0.57 to 0.84), although per-award funding was similar.

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.