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GenAI & Agentic AI

AI-Driven Trial Planning & Site Selection for Clinical Trials

Clinical trial data was fragmented across multiple registries and documents, making extraction of eligibility criteria, endpoints, and site information manual, time-consuming, and error-prone. This limited the ability to efficiently design protocols, identify optimal sites, and select suitable patient cohorts, resulting in delays and operational inefficiencies in trial planning

AI powered clinical trial site selection platform

Site selection is no longer uncertain—it is targeted, intelligent, and efficient

The Challenge

Fragmented Clinical Data and Manual Trial Planning Processes

Clinical trial data was scattered across registries, publications, and documents, requiring manual extraction and interpretation of eligibility criteria, endpoints, and site information. This time-intensive and error-prone process limited the ability to efficiently design protocols, identify optimal sites, and select suitable patient cohorts, resulting in delays and operational inefficiencies in trial planning

Solution

  • Implemented GenAI-powered data extraction and standardization to convert unstructured trial data into structured datasets mapped to MeSH, ATC, and CDISC standards

  • Built an autonomous data pipeline using Agentic AI to automate data discovery, ingestion, deduplication, entity resolution, and validation

  • Developed a decision intelligence engine to enable protocol optimization, site and investigator scoring, and patient cohort identification

  • Enabled AI-driven analytics for improved site selection and trial design decisions

  • Integrated proactive safety and risk monitoring to detect early safety signals and operational risks

AI analytics for clinical trial planning and protocol design

Impact

Faster Trial Planning, Improved Protocol Quality, and Optimized Enrollment Outcomes

  • Accelerated clinical trial planning and study design timelines

  • Improved protocol quality through upfront optimization of eligibility criteria and endpoints

  • Enhanced site selection and investigator matching for better enrollment outcomes

  • Increased diversity and representation in patient cohorts

  • Strengthened risk monitoring and regulatory readiness 

Measurable Impact

3–5x

faster trial planning cycles
(days vs weeks)

~30-40%

reduction in protocol amendments through upfront optimization

~20-30%

improved enrollment efficiency with targeted site and investigator matching

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