CTAD Lessons for 2020: More Phase 2 Trials, More Diversity

"Aaron Smith of Unlearn.AI, a startup company that uses machine learning to facilitate trials, suggested another way digital technology could cut enrollment. Machines can use data from the control arms of past trials to develop predictions for how disease will progress in a person depending on his or her baseline characteristics, he said. In effect, computers can generate a digital twin for each enrollee, describing what would likely happen to them without treatment. These digital twins can supplement the physical placebo group of a trial. This would lower the number of participants needed for a given trial, and allow a greater proportion of participants to receive drug rather than placebo. The chance of ending up in the placebo group deters many a potential participant."

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

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

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

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.
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Statistical principles of clinical trials with Digital Twins