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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Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍


Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍


Part 1: AI, Digital Twins, and the Future of Clinical Research‍

Learn about how Digital Twins are created and how they are incorporated into clinical trials to increase power, accelerate timelines, and enable patient level insights.
Watch an overview of specific use cases for Digital Twins and learn how novel trial designs with Digital Twins enable smaller trials that maintain their power.
Watch a panel discussion on the regulatory landscape where experts share perspectives on the future of AI-based drug development tools like Digital Twins.