Registered AI Medical-Imaging Clinical Trials on ClinicalTrials.gov: Publication Yield, Predictors, and Portfolio...

Although artificial intelligence (AI) clinical trials in medical imaging have grown rapidly, peer-reviewed publication outcomes and ClinicalTrials.gov portfolio shifts remain incompletely characterized.

From a ClinicalTrials.gov search on May 1, 2026, we assembled a historical cohort (n = 408; primary completion ≤ May 1, 2023) and a trend cohort (n = 835; first posted May 1, 2023 to May 1, 2026). Trials were classified by clinical intent (Checklist for Artificial Intelligence in Medical Imaging [CLAIM] 2024), modality, and anatomical region. Multivariable logistic and Cox regression evaluated publication and time to publication.

Peer-reviewed publication was identified in 79 of 408 historical-cohort trials (19.4%). Observational design (odds ratio [OR], 0.35; P =.001), inclusion of children (OR, 0.37; P =.018), and smaller planned enrollment (OR per log[enrollment + 1], 1.25; P =.006) were independently associated with publication likelihood, with concordant Cox findings. Unreported principal investigator (PI) location demonstrated an exploratory association (OR, 0.19; P =.023; n = 38). In the trend cohort, multi-modal imaging increased from 8.1% to 22.6%, industry sponsorship declined (11.8% to 6.6%; P =.003), and median planned enrollment rose from 300 to 400 (P <.001). China and the United States were the leading contributors, with notable European growth.

Peer-reviewed publication was identified for 19.4% of completed AI medical-imaging trials - a conservative estimate given registry-status incompleteness. Observational design, inclusion of children, and smaller planned enrollment were independently associated with lower publication likelihood. The portfolio is shifting toward multi-modal imaging and broader geographic representation.