Chronosight empowers biopharma and healthcare with AI/ML-driven risk modelling and clinical insights for fast, efficient, and accurate decisions.
AI-powered disease-focused embeddings, causal-ML, and predictive models drive disease space interpretation, subgroup detection, and early-derived endpoints.
Chronosight integrates real-world and synthetic data to deliver robust risk curves, survival-style estimates, and cohort-level metrics — validated on independent populations.
By modelling multi-disease progression at population scale, Chronosight reveals drug-disease relationships and patient subgroups invisible to traditional methods.
Identify biomarker-defined patient populations with elevated or differential response using causal-ML embeddings.
Sample synthetic future health trajectories up to 20 years forward — enabling in-silico trial simulation and power analysis.
AI-derived early endpoints reduce trial length and cost while maintaining statistical validity and regulatory acceptance.
Our platform delivers measurable advantages across every stage of clinical development.
Increase success rates with AI-powered risk optimization and predictive modelling that identifies the right patients and endpoints early.
Make data-driven decisions backed by advanced analytics and causal inference — move from intuition to evidence at every decision point.
Leverage historical and real-world data to inform and optimize risk estimates — unlock the full value of data you already have.
Streamline operations and reduce time-to-market with efficient protocols designed around AI-derived endpoints and adaptive designs.
Explore disease space, real-world rates, and person-level risk in an integrated analytics platform.
Explore disease embeddings and UMAP projections. Run model evaluations and inspect disease taxonomy with AI-powered visualization.
Explore →Visualize real-risk data and disease-specific rates. Identify subgroups and biomarkers with interactive scatter plots and time-to-event analytics.
Explore →Person-level risk estimates and cohort analytics. Filter by disease, date range, and risk measures with AI-driven causal modelling.
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Learning the natural history of human disease with generative transformers
A GPT-based model of multi-disease progression that predicts 1,000+ disease rates and samples future health trajectories. Trained on UK Biobank, validated on 1.9M Danish individuals with no parameter changes. Achieves average AUC of ~0.76 across age-stratified predictions.
Altmetric score 1,714 — covered by Nature News, Scientific American, Handelsblatt, Videnskab.dk and 50+ outlets across 6 languages and 5 continents.
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Validated with leading research institutions including DKFZ and EMBL — ensuring scientific rigour and independent validation.