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.

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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
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article

Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023

Shoko Maru, Ross J. Simpson, Ryohei Kuwatsuru, Michael Matthias
9 citations
Journal of Medical Internet Research
Artificial Intelligence in Healthcare and Education
1.08
article

Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023

Shoko Maru, Ross J. Simpson, Ryohei Kuwatsuru, Michael Matthias
article en
9 citations

Abstract

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.

Journal of Medical Internet ResearchVol. 26
University of North Carolina at Chapel Hill (US), Juntendo University (JP), Matrix Engineering (United States) (US)
Openalex Percentile: Top 21%
Artificial Intelligence in Healthcare and Education
1.08
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