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Registered AI Medical-Imaging Clinical Trials on ClinicalTrials.gov: Publication Yield, Predictors, and Portfolio
Shuqin Sun1, Chengbin Tian1, Jinping Peng1
1Department of Radiology, General Hospital of Pingmei Shenma Medical Group, Pingdingshan, Henan, China (S.S., C.T., J.P.).
Academic Radiology
|July 10, 2026
Summary
Publication rates for artificial intelligence (AI) medical imaging trials are low, with observational designs and pediatric focus linked to reduced publication likelihood. The field is expanding to multi-modal imaging and global participation.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Clinical Trials
Background:
- Artificial intelligence (AI) in medical imaging clinical trials has seen rapid growth.
- Characterizing publication outcomes and ClinicalTrials.gov portfolio trends is crucial but incomplete.
Purpose of the Study:
- To analyze publication rates of completed AI medical imaging trials.
- To identify factors associated with publication success.
- To examine shifts in the AI medical imaging trial portfolio over time.
Main Methods:
- Assembled historical (n=408) and trend (n=835) cohorts from ClinicalTrials.gov.
- Classified trials by clinical intent (CLAIM 2024), modality, and anatomy.
- Used logistic and Cox regression to evaluate publication and time to publication.
Main Results:
- 19.4% of historical AI medical imaging trials were published.
- Observational design, inclusion of children, and smaller enrollment were linked to lower publication likelihood.
- The trend cohort showed increased multi-modal imaging, decreased industry sponsorship, and rising enrollment, with growth in China, the US, and Europe.
Conclusions:
- Publication rates for AI medical imaging trials are approximately 19.4%, potentially underestimated.
- Trial design features like observational status and pediatric focus negatively impact publication.
- The AI medical imaging trial landscape is evolving towards multi-modal approaches and wider geographic involvement.
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