Unlearn.AI

Unlearn.AI is an AI platform that generates digital twins of clinical trial participants — individualized virtual patient models trained on historical clinical data — to increase the statistical efficiency of randomized controlled trials without reducing scientific rigor. Its core methodology, PROCOVA, has received qualification opinion from the European Medicines Agency, making it the first AI methodology in clinical trials to achieve EMA regulatory recognition. The platform supports neurodegenerative, metabolic, immunological, and oncological disease areas, with validated models covering Alzheimer's disease, ALS, Parkinson's, asthma, and diabetes, among others. Controlled trials face a fundamental efficiency problem: large control groups are required for statistical power, but exposing more participants to placebo or standard of care instead of experimental treatment is ethically costly and slows enrollment. Unlearn.AI's Digital Twin Generators (DTGs) address this directly. For each enrolled participant, the platform constructs a digital twin that forecasts their individualized disease trajectory under control conditions, generating a personalized prognostic score used as a covariate in the primary statistical analysis. This approach reduces residual variance and increases statistical power without requiring more patients — enabling sponsors to either reduce control group size or improve the sensitivity of interim looks and subgroup analyses. Unlearn.AI's TrialPioneer platform, launched as a unified clinical development workspace, brings together literature search, regulatory precedent analysis from PubMed and ClinicalTrials.gov, trial design modeling, and digital twin forecasting into a single environment. In 2026, FDA-aligned use of Unlearn's digital twins as Phase 2/3 synthetic control arms in neurodegenerative disease is an active and expanding area of regulatory precedent. The platform is specifically designed for biopharmaceutical sponsors, CROs, and academic research centers with access to historical patient-level data for the disease area in question. Organizations without meaningful prior clinical datasets in the target indication will face limitations in DTG model accuracy, since the quality of digital twin forecasts is bounded by the historical data available for training. Solo investigators or resource-limited research teams without biostatistics infrastructure will also find deployment of PROCOVA methodology challenging without dedicated support.

Unlearn.AI creates AI-powered digital twins of clinical trial patients, enabling smaller control groups, faster enrollment, and higher statistical power in Phase 2 and 3 studies.

Unlearn.AI is widely used by professionals, developers, marketers, and creators to enhance their daily work and improve efficiency.

Unlearn.AI is an AI Tool that applies generative machine learning to the design and execution of clinical trials, producing individualized digital twin forecasts that increase statistical efficiency and reduce ethical burden in randomized controlled studies. The EMA-qualified PROCOVA methodology and active use in Phase 2/3 neurodegenerative and oncology trials demonstrate regulatory maturity that distinguishes the platform from earlier-stage clinical AI research tools. Analysts estimate the digital twins market in clinical research will reach $1.8 billion by 2028, with Unlearn as one of the established commercial platforms in that space.

It is suitable for beginners as well as professionals who want to streamline their workflow and save time using advanced AI capabilities.