Steven Herne
CEO
Unlearn
Unlearn.ai develops machine-learning models that create digital twins of clinical trial participants, enabling the use of synthetic patient data to enhance control groups. This technology accelerates clinical trials by allowing smaller study designs while maintaining statistical power, ultimately reducing the time and cost associated with bringing new therapies to market.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
- Enterprise Software
- Healthcare Technology
- Software Only
Updated 2 days ago
Funding
Raised to date
$134.9MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Traditional clinical trials often require large control groups, leading to increased costs, longer timelines, and ethical concerns related to exposing patients to ineffective treatments. Inefficient trial designs can also hinder the development and approval of new therapies, particularly for diseases with limited patient populations.
Solution
Unlearn.ai offers a platform that leverages machine learning to create digital twins of clinical trial participants, enabling the use of synthetic control arms. The platform's Digital Twin Generators use AI to predict individual patient outcomes, enhancing the power of clinical trials while reducing the required sample size. By integrating digital twins into trial design, Unlearn.ai accelerates clinical development, reduces costs, and facilitates more informed decision-making throughout the trial process. The platform is designed to optimize study endpoints, enhance sensitivity at both population and subpopulation levels, and de-risk trial design by simulating protocol scenarios.
Target Audience
The primary target audience includes pharmaceutical companies, biotech firms, and clinical research organizations (CROs) seeking to accelerate clinical trials, reduce development costs, and improve the efficiency of their research programs.
Features
- AI-powered Digital Twin Generators that create synthetic patient data to augment or replace traditional control groups
- Predictive models that forecast individual patient outcomes for all measured clinical variables at any point in time
- Simulation tools for optimizing trial design, including inclusion/exclusion criteria and endpoint strategies
- Integration with existing clinical trial workflows and data management systems
- Support for various therapeutic areas, including neuroscience, immunology, and metabolic diseases
- EMA qualification and alignment with current FDA guidance on the use of synthetic control arms