THE TRANSFORMATIVE ROLE OF AI IN CLINICAL TRIAL MANAGEMENT: FROM PATIENT RECRUITMENT TO REAL-TIME MONITORING

Artificial intelligence (AI) is rapidly transforming clinical trial management by addressing long‑standing operational, scientific, and ethical challenges across the trial lifecycle. AI-driven tools enable more efficient patient recruitment and identification through automated screening of electronic health records, real‑world data, and unstructured clinical narratives to match participants precisely with complex eligibility criteria while supporting greater diversity and representativeness. In parallel, machine learning and natural language processing techniques optimize protocol design, site selection, and feasibility assessments, reducing startup times, minimizing protocol amendments, and improving the likelihood of trial success. AI systems also enhance real-time monitoring, data quality, and safety oversight by integrating data from remote monitoring technologies, wearables, and electronic data capture platforms, enabling early detection of protocol deviations and adverse events. Moreover, emerging applications such as digital twins, synthetic control arms, and federated learning hold promise for further accelerating evidence generation while preserving patient privacy and maintaining regulatory standards. Despite these advances, widespread adoption remains constrained by data governance issues, algorithmic bias, transparency and validation requirements, and evolving regulatory expectations. This review critically examines the current and emerging roles of AI in clinical trial management, with emphasis on patient recruitment and identification, operational optimization, data integrity, and safety monitoring, and outlines future directions for responsible integration into drug development.