Korean J Clin Pharm.  2024 Jun;34(2):134-139. 10.24304/kjcp.2024.34.2.134.

Trends in Artificial Intelligence Applications in Clinical Trials: An analysis of ClinicalTrials.gov

• Go JM
• Lee JY
• Song YK
• Kim JH

• KMID: 2564368
• DOI: http://doi.org/10.24304/kjcp.2024.34.2.134

Abstract

Background
Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies.
Methods
As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection.
Results
The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to develop rapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Keyword

Artificial intelligence; clinical trial; clinicalTrials.gov; endpoint assessment; machine learning