AI/ML in Clinical Trials: Enhancing Data Integrity and Decision-Making

Said Khan

Said Khan

Keywords: Artificial Intelligence, Machine Learning, Clinical Trials, Data Integrity, Decision-Making, Data Quality Assurance, Regulatory Compliance, Participant Engagement, Real-Time Monitoring, Anomaly Detection, Adaptive Governance, Ethical Considerations

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into clinical trials has ushered in a new era of data-driven research and decision-making. This paper explores how AI/ML innovations are enhancing data integrity and transforming the landscape of clinical trials. Through a comprehensive review of literature and practical case studies, the paper highlights strategies for ensuring data quality, maintaining regulatory compliance, and fostering participant engagement. The role of real-time monitoring, anomaly detection, and adaptive governance frameworks in bolstering data integrity is examined, along with ethical considerations in AI/ML deployment. The paper concludes with insights into the transformative potential of AI/ML in clinical trials, offering a roadmap for researchers and stakeholders to navigate the complexities of this rapidly evolving field.