Using AI-based Prognostic Models to Design Efficient, Unbiased Clinical Trials

Recent progress in Artificial Intelligence and Machine Learning provides an avenue for using historical data to create more efficient clinical trials without introducing bias. Rather than incorporating data from external sources directly into the trial, we leverage predictions from AI-based prognostic models — called Digital Twins — trained on historical control data to reduce uncertainty in estimated treatment effects. In this whitepaper, we describe how this novel approach enables optimally efficient clinical trials that require fewer subjects to achieve pre-specified power while rigorously controlling bias and type-I error rates.

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Applying Machine Learning to Increase Clinical Trial Efficiency: A Regulatory Journey

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Unlearn Closes $50 Million Series B Funding to Advance the Use of Its Machine Learning-Powered TwinRCTs™ in Clinical Trials

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Unlearn Signs Multi-Year Collaboration with Merck KGaA, Darmstadt, Germany to Accelerate Immunology Trials using Twintelligent RCTs™

Collaborators will leverage AI-generated Digital Twins to enable smaller, more efficient pivotal clinical trials.
Led by Insight Partners, financing builds on the company’s momentum in working with leading biopharmaceutical companies to improve clinical trial efficiency.
Learn about the evolution of ideas that led to our TwinRCT™ solution for smaller, more efficient clinical trials and recent EMA draft qualification opinion.