P233: Enhancing Clinical Trial Oversight Using AI-Driven Automated Insights
Poster Presenter
Rohit Kadam
Researcher, TCS ADD
TATA CONSULTANCY SERVICES LIMITED, INDIA India
Objectives
To describe an AI-powered agentic clinical monitoring approach that enables proactive, real-time identification of safety and operational risks, reducing reliance on retrospective reviews and improving decision-making in complex clinical trials.
Method
An AI agentic framework was designed to analyze patient-, site-, and study-level clinical trial data across visits and sources, automatically interpreting and summarizing signals to support CRAs and medical monitors within RBQM-aligned workflows.
Results
The AI-powered agentic monitoring system demonstrated the ability to continuously analyze large volumes of patient-, site-, and study-level data and generate concise, actionable insights for clinical stakeholders. By automating interpretation and summarization of complex datasets, the system enabled earlier identification of emerging safety signals, operational risks, and data quality concerns compared with traditional retrospective monitoring approaches.
The multi-agent architecture reduced the cognitive burden on CRAs and medical monitors by synthesizing information across multiple visits and data sources into coherent risk narratives. This supported faster review cycles, improved focus on high-risk areas, and enhanced consistency in monitoring decisions. The solution aligned with Risk-Based Quality Management principles by facilitating proactive risk detection and timely mitigation rather than reactive issue resolution.
Conclusion
AI-driven agentic monitoring can transform clinical trial oversight from a reactive, resource-intensive process into a proactive and insight-driven model. By enabling real-time risk identification, reducing manual review effort, and supporting RBQM compliance, this approach has the potential to improve patient safety, data integrity, and operational efficiency in increasingly complex clinical trials.
