CurePilot.AI: A Generative Artificial Intelligence - Driven Copilot for Optimizing End-to-End Clinical Trial Operations
Abstract
Mouna Nataraja, Gurucharan Iyer and Priya Singh
Clinical trials are the cornerstone of medical innovation, yet they remain constrained by manual processes, long timelines, high operational costs, and fragmented data systems. Inefficiencies in patient recruitment, protocol design, and data monitoring often delay research outcomes and increase administrative burden. To address these challenges, this study introduces CurePilot.AI, a Generative-AI-driven Clinical Trial Copilot designed to optimize and automate the end-to-end clinical trial workflow.
CurePilot.AI integrates natural language processing, predictive analytics, and generative automation to support key trial functions including protocol design, patient selection, data management, statistical analysis, and regulatory documentation. The platform consolidates heterogeneous clinical and operational data into a unified, real-time dashboard that enhances transparency, accuracy, and decision support across all stakeholders. By automating repetitive and documentation-intensive tasks, it reduces trial delays, minimizes human error, and ensures compliance with global regulatory frameworks.
The proposed system demonstrates how generative AI can transform conventional research operations into an intelligent, data-driven, and compliant clinical trial ecosystem, accelerating the development of safe and effective therapies while maintaining integrity and traceability throughout the process.
