Clinical Trial Optimization

Use AI to optimize clinical trial design, recruitment, and operational efficiency for faster drug development

Business impact

• Time to market — Shorten drug development cycles by accelerating trial phases and decisions
• Clinical trial success rate — Increase likelihood of trial success through better patient and protocol matching
• Operational efficiency — Streamline workflows and reduce manual effort in trial management
• Cost reduction — Lower expenses by optimizing site selection and resource allocation
• Enrollment rates — Improve patient recruitment speed and accuracy for timely trial completion

Data requirements

• Biomedical knowledge graphs (Structured) — Use curated biological relationships to inform trial criteria
• Clinical trial operational data (Numeric) — Analyze site performance and patient recruitment metrics
• Scientific literature and publications (Text) — Extract validated evidence to support drug indications
• Patient demographics and health records (Structured) — Identify eligible participants and stratify cohorts
• Trial logistics and supply chain data (Numeric) — Optimize resource allocation and scheduling

AI methods and techniques

• Predictive AI — Forecast trial outcomes and patient enrollment success
• Generative AI — Create digital twins of trial sites and simulate scenarios
• Agentic AI — Automate trial workflow management and decision-making

AI models and model families

GPT-4o, Claude, Neo4j graph-grounded AI, Quantum-enhanced Markov Chain Monte Carlo

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: Pharma Essentia, Quantum X Labs Inc, Ryght AI.