A multi-agent AI platform for clinical trial operations: Specialized agents to accelerate feasibility assessment while ensuring data accuracy.

Abstract

e13646 Background: Clinical trial feasibility assessment typically requires 3-4 weeks of manual data gathering across regulatory databases, site performance records, and protocol requirements. While large language models offer potential automation, single-model approaches risk data hallucination and inconsistent outputs across complex multi-source queries. We developed a multi-agent AI platform where specialized agents query validated databases under human expert oversight to accelerate feasibility workflows while maintaining data integrity. Methods: The platform employs a five-layer architecture with four specialized AI agents: (1) Query Agent converts natural language questions into structured database calls with 98% intent accuracy; (2) Compliance Agent validates outputs against GCP, ICH-E6, and 21 CFR Part 11 requirements; (3) Analytics Agent performs statistical correlation and anomaly detection across historical data; (4) Reporting Agent generates formatted documents from validated data sources. Critical differentiator: agents retrieve data from actual databases rather than generating information, eliminating hallucination risk at the architectural level. A human-in-the-loop (HITL) orchestration layer routes queries to appropriate agents and presents results for expert verification before finalization. Results: Deployment across 23 feasibility studies demonstrated: feasibility cycle time reduced from 28 to 11 days (61% reduction); report generation time decreased from 8 hours to 45 minutes (91% reduction); 98% natural language query accuracy; 23 regulatory compliance gaps identified that manual review missed; 100% QA audit pass rate with zero data integrity findings. HITL checkpoints showed 14% modification rate by human experts, with changes primarily reflecting local regulatory nuances (42%), recent personnel updates (31%), and sponsor-specific preferences (27%) not captured in training data. Conclusions: A multi-agent architecture with specialized database-querying agents and human oversight achieves 61-91% efficiency gains in clinical trial feasibility assessment while eliminating AI hallucination risk. The 14% HITL modification rate validates the necessity of expert oversight while demonstrating that AI can reliably handle 86% of routine queries. This approach offers a scalable model for AI-augmented clinical operations that maintains regulatory compliance and data integrity.