Clinical Trial Optimization

Patient matching, protocol simulation, and outcome prediction using biomedical data and AI models optimize clinical trials.

Business impact

• Clinical trial efficiency — Improves patient matching and protocol design to shorten trial durations and costs
• Cost reduction — Lowers expenses by optimizing trial logistics and reducing failed trial rates
• Likelihood of trial success — Increases success probability by predicting outcomes and refining trial parameters

Data requirements

• Biomedical knowledge graphs (Structured) — Integrate curated biomedical relationships to identify drug-disease connections
• Genomic and electronic health records (Structured) — Provide patient genetic and clinical data for population stratification
• Pathology images (Image) — Extract high-dimensional features to discover biomarkers and trial endpoints
• Clinical trial protocol data (Structured) — Simulate and evaluate protocol variations to optimize trial design

AI methods and techniques

• Predictive AI — Forecast trial outcomes and patient responses to optimize trial parameters
• Generative AI — Generate synthetic patient cohorts and simulate trial scenarios for planning
• Agentic AI — Automate site selection and patient matching workflows with adaptive decision-making

AI models and model families

Llama 3, GPT-4o, QuantHealth Clinical-Simulator, Large-language multi-modal models

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: ConcertAI, QIAGEN Digital Insights, QuantHealth, Quantum X Labs Inc, Truveta.