AI Clinical Trials Registered on ClinicalTrials.gov Showed Persistent Misalignment with Global Disease Burden,...

To determine whether artificial intelligence (AI) clinical trial activity aligns with global disease burden, and whether the 2023 launch of a global AI health equity initiative was associated with a reorientation of research priorities.

We conducted a cross-sectional metaresearch study mapping 2,771 AI clinical trials registered on ClinicalTrials.gov between 2010 and 2026 to Global Burden of Disease (GBD) Level 3 disease causes, quantifying mismatch between trial allocation and burden across three metrics: disability-adjusted life years (DALYs), deaths, and prevalence.

Log-log regression of trial count on global DALYs returned a slope of 0.31, indicating that a 10-fold increase in disease burden was associated with only a 2.0-fold increase in AI trial activity against an expected slope of 1.0 under proportional allocation. Slopes for deaths (0.56) and prevalence (0.14) showed similar subproportional patterns. Fifty-six of 160 GBD Level 3 causes (35.0%) had no AI trials identified by our title-based search criteria across the entire study period, collectively representing 14.4% of global DALYs; diarrheal diseases alone carried 56.5 million DALYs with zero trials. Among causes with at least one trial, inflammatory bowel disease (mismatch ratio 12.94), non-melanoma skin cancer (12.41), and alopecia areata (19.06, n = 2 trials) were among the most over-represented relative to their burden. Neonatal disorders (0.05), malaria (0.02), and tuberculosis (0.10) were the most under-represented. Mismatch patterns were broadly consistent across burden metrics (DALY vs. deaths Spearman r = 0.77; DALY vs. prevalence r = 0.60). To assess whether the July 2023 launch of the Global Initiative on AI for Health (GI-AI4H) by WHO, ITU, and WIPO was associated with a reorientation of research priorities, we compared mismatch ratios before and after July 5, 2023. Among 75 eligible causes, 32 (42.7%) experienced worsened mismatch and 43 (57.3%) improved; however, the largest shifts reflected reductions in extreme over-representation among already heavily researched conditions rather than gains for high-burden neglected ones.

AI clinical trial activity remains substantially misaligned with global disease burden. No observable reorientation toward high-burden neglected conditions was detected in registration data following the establishment of a formal international equity mandate.

Artificial intelligence is increasingly used in medical research, but it is unclear whether AI clinical trials focus on the diseases that cause the most harm worldwide. We looked at every AI-related clinical trial registered on the website ClinicalTrials.gov between 2010 and 2026 and compared how many trials existed for each disease against how much illness, death, and disability that disease causes globally. We found that AI trials often focus on diseases that are already well studied, such as certain skin conditions and some cancers, while diseases that cause enormous suffering worldwide, such as diarrheal diseases, malaria, and newborn health problems, had few or no identified AI trials at all. In fact, 56 diseases that together account for more than one in every seven years of healthy life lost worldwide had no identified AI trials studying them. Diseases that cause more global harm did tend to attract more AI trials overall, but not nearly in proportion: a tenfold increase in a disease's burden was linked to only about a twofold increase in AI trial activity. In 2023, the World Health Organization and other international bodies launched an initiative meant to direct AI health research toward the diseases most in need. We compared AI trial activity before and after this initiative launched and found no clear shift toward the neglected diseases it aimed to help. These findings suggest that decisions about which diseases to study with AI are being shaped more by where research is already easy or already funded than by where AI could help the most people. Funders and researchers may need to take deliberate steps to direct AI research toward high-burden, understudied diseases if this pattern is to change.