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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Why can’t we agree on how to define digital twins in healthcare?

White Papers

Summary of the EMA September 2022 Qualification Opinion for PROCOVA™


Charles Fisher, Unlearn.AI: “now is the time to adopt AI-based solutions”

The potential for AI implementation in healthcare can barely be measured, as it can already do what humans do, just countless times better and more efficiently.
The European Medicines Agency has qualified Unlearn’s AI-powered method for running smaller, faster clinical trials.
Digital twins seem to be everywhere in healthcare now, but no one agrees on a single definition for them.