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.