Funding for AI in Surgical Specialties Does Not Align with Disease Burden

Gorijavolu R, Cho A, Boparai S, Johal A, et al. NIH Funding for Artificial Intelligence in Surgical Specialties: Allocation and Alignment with US Disease Burden. J Am Coll Surg. June 2026.

National Institutes of Health (NIH) investment in artificial intelligence (AI) research within surgical specialties is rapidly expanding, yet whether funding aligns with disease burden or reflects structural disparities remains unclear.

Cross-sectional analysis of NIH RePORTER (1993–2024) identified 1,657 AI grants across eight surgical specialties. Multivariable regression analyses of 976 grants awarded to 379 investigators between 2021 and 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: 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.

These findings highlight structural disparities in funding allocation and establish a benchmark for policy efforts aimed at better aligning AI research investment with population health needs.