Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.
Recruitment and retention challenges continue to hinder the success of clinical trials. Artificial intelligence (AI) has emerged as a promising means to optimize various clinical trial processes; however, its impact specifically on recruitment and retention has not been comprehensively evaluated. This scoping review utilized the Joanna Briggs Institute framework and adhered to PRISMA-ScR guidelines, systematically searching literature published between January 2018 and June 2024 across multiple databases. Of the 21,573 records screened, 121 studies were included. A meta-analysis was conducted to quantitatively assess the performance of AI-driven tools. AI applications for patient screening demonstrated strong performance, achieving a pooled sensitivity of 0.91 (95% CI: 0.84-0.95) and an area under the curve (AUC) of 0.79 (95% CI: 0.72-0.85). AI tools employed for eligibility identification and classification also exhibited strong outcomes, with pooled sensitivities of 0.80 (95% CI: 0.76-0.84) and 0.92 (95% CI: 0.84-0.96), respectively, and precisions of 0.84 (95% CI: 0.80-0.88) and 0.91 (95% CI: 0.85-0.95). AI tools aimed at identifying patient cohorts showed moderate effectiveness (pooled sensitivity: 0.70 [95% CI: 0.52-0.84]; AUC: 0.74 [95% CI: 0.61-0.84]). Overall, AI presents significant potential for enhancing clinical trial recruitment and retention, with effectiveness varying across specific applications. These findings underscore AI's valuable role in improving trial efficiency and data quality.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Citation