Unlearn
AI digital twins of clinical trial participants to help design faster, smaller trials.
Introduction
Unlearn uses AI to build digital twins of clinical trial participants, which are used to predict outcomes for each participant. Researchers can design smaller trials, explore predicted results for participant subgroups, and simulate protocol scenarios early in planning. The aim is to reduce trial risk and shorten timelines.
- Added on
- Nov 2, 2026
- Website
- unlearn.ai
- Pricing model
- Contact for Pricing
- Company
- Unlearn.ai
Product information
What is Unlearn?
Unlearn's platform uses AI to create digital twins of clinical trial participants. The twins are used to predict outcomes over time and to support decisions about trial design.
How to use Unlearn?
On the platform, you can design and run smaller clinical trials, review predicted outcomes for participant subgroups, and simulate protocol options at the early design stage. A demo can be booked to see how it fits a particular study.
Core features of Unlearn
- Digital twin generation for trial participants
- Outcome prediction with high precision
- Trial design simulation
- Real-time insights for data-driven decisions
Use cases of Unlearn
- 1Designing smaller, more powerful trials
- 2Exploring predicted outcomes for participant subgroups
- 3Simulating protocol options before a trial starts
Frequently asked questions about Unlearn
What does the Unlearn platform do?+
It uses AI to create digital twins of clinical trial participants, which helps improve trial efficiency and the insight gained from each study.
How does Unlearn help shorten clinical trial timelines?+
It supports the design of smaller trials that keep or increase statistical power.
How does Unlearn help de-risk trial design?+
Digital twins are used to simulate protocol scenarios early in the design stage.
Which category is Unlearn in?+
Unlearn is listed under AI Research Assistant, AI Health & Wellness.
Tags
ai healthcareai research toolai predictionsdigital twinsclinical trialsUnlearn alternatives
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