Applications of Digital Twins in Clinical Trials for Alzheimer’s Disease

AI-driven clinical trials have the potential to achieve higher power with fewer subjects, to provide actionable insights into which patients achieve the greatest benefit, and to design faster, more efficient clinical trials - all while ensuring reliable evidence about safety and efficacy. Digital Twins, or simulated control outcomes for individual patients, can be used to design trials that are smaller, faster, and/or more powerful. In this whitepaper, we describe 4 novel trial designs that balance three different characteristics: (i) the number of subjects required to achieve the power to detect a pre-specified effect size, (ii) insensitivity to potential confounding factors, and (iii) the type-I error rate, to demonstrate how Digital Twins can accelerate both early and late-stage clinical trials.

Enter your email address to download paper.

Click the link to begin download.
Oops! Something went wrong while submitting the form.

Enter your email address to watch the webinar.

Click the link to watch webinar.
Oops! Something went wrong while submitting the form.

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.