Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023
Much of the rapid growth in AI/ML studies comes from high-income countries in high-resource settings, albeit with a modest increase in upper-middle-income countries (mostly China). Lower-middle-income or low-income countries remain poorly represented. The increase in randomized or prospective designs, along with 738 large studies (n>1000), mostly ongoing, may indicate that enough studies are shifting from an in silico evaluation stage toward a prospective comparative evaluation stage. However, the ongoing limited availability of basic results on ClinicalTrials.gov contrasts with this field's rapid advancements and the public registry's role in reducing publication and outcome reporting biases.
Authors
- Shoko Maru (ORCID: https://orcid.org/0000-0002-4722-4111)
- Ross J. Simpson (ORCID: https://orcid.org/0000-0001-6938-8435)
- Ryohei Kuwatsuru (ORCID: https://orcid.org/0000-0003-1960-6400)
- Michael Matthias
Institutions
- University of North Carolina at Chapel Hill (US)
- Juntendo University (JP)
- Matrix Engineering (United States) (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-08-16
- DOI
- https://doi.org/10.2196/57750
- Citations
- 9
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.08