Unlearn.AI
Streamline clinical trials with AI and digital twins of patients
Tool Information
What is Unlearn.AI?
Unlearn.AI is an AI platform that provides a scientific intelligence layer for clinical development. It integrates evidence, modeling, and AI-generated digital twins of patients to help trial teams plan, monitor, and analyze clinical trials with greater confidence.
Core Features
Scout
Continuously searches, structures, and summarizes relevant literature and regulatory precedent from sources like PubMed, ClinicalTrials.gov, and drugs@FDA in one place to eliminate scattered searches and align on precedent faster.
Hindsight
Explores harmonized clinical trial and real-world datasets to validate clinical and statistical assumptions, assess population characteristics, endpoint behavior, and benchmarks.
SimLab
Builds and compares trial-design scenarios to evaluate endpoints, inclusion/exclusion criteria, sample size, and constraints, with every scenario reproducible and linked to underlying evidence.
Anomaly Detection and Clinical Data Surveillance
Provides continuous patient- and site-level monitoring of clinical data to detect anomalies such as unexpected values, off-trajectory responders, and multi-variate signals benchmarked against historical patient trajectories.
Digital Twins for Trial Analyses
Uses AI-generated forecasts of clinical trial participants’ expected control outcomes as external comparators or to support smaller sample sizes and increased power in randomized trials, with methodology qualified by the EMA and aligned with current FDA guidance.
Use Cases
- #1
Trial planning and design
Supports upstream trial design by bringing together literature search, dataset exploration, and scenario simulation for iterative decisions on endpoints, criteria, and sample size.
- #2
Clinical data monitoring
Enables continuous anomaly detection and surveillance of patient and site-level clinical data during trials.
- #3
Trial analysis with digital twins
Strengthens analyses in early-stage, open-label, and randomized trials by using digital twins to reduce variability, improve treatment effect detection, and support reductions in trial size, cost, and time to readout.
- #4
Accelerating development in specific therapeutic areas
Applied in neuroscience, immunology, metabolic disease, Alzheimer’s disease, and ALS studies to support smarter designs and faster milestones.
Alternatives
Frequently Asked Questions
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How can I contact Unlearn?
Unlearn.AI Contact & Social Media
- CompanyUnlearn
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