Leveraging AI-Driven Anomaly Detection for Enhanced Data Quality and Regulatory Compliance in Clinical Studies

Abstract

The integration of artificial intelligence-driven anomaly detection systems has revolutionized data quality management and regulatory compliance in clinical studies. By leveraging advanced machine learning algorithms and pattern recognition capabilities, these systems enhance the detection and prevention of data inconsistencies while ensuring adherence to regulatory guidelines. The implementation demonstrates marked improvements in adverse event reporting, protocol deviation monitoring, and data standardization processes. Through automated validation frameworks and real-time monitoring capabilities, organizations can significantly reduce manual intervention requirements while maintaining high standards of data integrity. The evolution from traditional manual processes to AI-enabled monitoring represents a fundamental transformation in how clinical data quality is managed, leading to enhanced patient safety outcomes and more efficient trial operations.