This white paper focuses on applying AI and machine learning to improve efficiency across the clinical development lifecycle. It highlights common bottlenecks such as manual data review, delayed signal detection, and fragmented systems. AI-driven analytics enable faster identification of safety signals, protocol deviations, and operational risks through continuous monitoring and advanced visualization. The paper emphasizes centralized data platforms that harmonize clinical, operational, and external data sources, making them ready for AI analysis. By embedding AI into clinical workflows, organizations can enhance trial quality, reduce costs, and make proactive, data-driven decisions that improve patient safety and trial outcomes.

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