Clinical trials are essential for generating evidence in drug development, yet they continue to face persistent challenges including high costs, lengthy timelines, and low success rates. Recent advances in artificial intelligence (AI) and its transformative impact across medicine have highlighted its potential to address these limitations. Accordingly, AI is being increasingly integrated throughout the clinical trial lifecycle, enabling innovations in study design, patient recruitment, trial monitoring, data analysis, outcome prediction, and operational decisionmaking. In this review, we provide a comprehensive overview of current and emerging AI applications in clinical trials, emphasizing their potential to improve efficiency, enhance trial quality, and accelerate evidence generation. We further examine key practical considerations for implementation, including data quality, model interpretability, regulatory requirements, ethical concerns, and barriers to real-world adoption. Finally, we discuss future directions for AIenabled clinical trials, highlighting opportunities for improved scalability, generalizability, and clinical impact. By synthesizing recent advances and ongoing challenges, this review aims to guide researchers and practitioners navigating the rapidly evolving landscape of AI in clinical trials and to provide actionable insights for AI researchers, clinicians, patient advocates, trial investigators, and drug developers seeking to integrate AI into clinical research practice.
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