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/Index/Unlearn.AI/A model asset rather than a raw corpus
U

A model asset rather than a raw corpus

UNLEARN.AIManage supplier listing

A model asset rather than a raw corpus: trained generative digital-twin models that forecast an individual participant's clinical outcome trajectory, built per therapeutic area from sponsor clinical trial data and natural-history data. Published evidence areas include ALS, Alzheimer's and a pan-cancer foundation model. The licensable artefacts are the fitted patient-progression generators, the synthetic control/cohort populations they emit, and the validation evidence set that was used to demonstrate twin accuracy against real trial arms

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Unlearn.AI/A model asset rather than a raw corpus
SampleCoverage

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Coverage

Universe, instruments and categories.

Industry
Health Care Technology
Instruments
equities
Categories
AlternativeReference
Regions
US
Sample tickers
ABBVMRKPFENVSLLYBIIBREGNGILD
Dataset card
Type
not stated
Format
Tabular longitudinal synthetic data — simulated per-patient outcome trajectories, disease-progression time series and cohort-level event sequences — plus the model weights and configuration of the generators themselves, and validation reports comparing simulated against observed arms
Volume
Not published. Bounded read: volume is measured in modelled participants per engagement rather than a stored corpus — a single Phase III digital-twin comparison typically simulates a control arm in the hundreds to low thousands of participants, and the evidence library spans multiple disease programmes across ALS, Alzheimer's and oncology. The generator, not a dataset, is the accumulated asset
Users
Not disclosed; a ~73-person Series C company at roughly $5M revenue implies a small number of marquee sponsor engagements rather than a broad user base — consistent with the named large-pharma collaborations and disease-programme case studies the site highlights. There is no self-serve seat model
History
not stated
Update frequency
not stated
Growth
Active — a 2026 industry award for clinical trial design, a visibly expanding evidence library across disease areas including a 2026 pan-cancer foundation-model presentation, ongoing sponsor collaborations, and a restructured site organised around three commercial entry points
Launched
~2018 (founded in San Francisco on the digital-twin concept; the first public demonstrations of twin-based control arms followed a couple of years of model development)
Delivery
not stated
Entity mapping
not stated
Sample
not stated
Point-in-time
not stated
Licence
not stated

supplier index read from their site

Unlearn.AI has not added their own details yet. Not yet on file:

  • Dataset
  • Data dictionary
  • Coverage
  • Provenance
  • Rights & data handling