# Owkin

Source: https://onco.cc/companies/owkin/  
OnCo record `owkin` (Company). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

French AI biotech using federated learning across hospitals; first CE-marked AI for MSI prediction from H&E.

## Summary

Owkin, based in Paris and New York, is a French AI biotechnology company that uses federated learning across hospitals and produced the first CE-marked AI for predicting microsatellite instability from routine H&E slides. Its products include MSIntuit CRC, RlapsRisk BC for breast cancer relapse risk, the K1.0 agentic biology platform and the Phikon pathology foundation models, alongside federated research consortia. OnCo links it to digital pathology, federated learning and privacy-preserving AI, digital twins and spatial-omics-guided treatment, and to ideas on a federated learning consortium of cancer centres that jointly own the models. Data silos and AI that is built but not validated are the bottlenecks it addresses. Whether federated training can match centralised data on model quality is the open question.

## Fields

- Kind: Company
- Last checked: 2026-09-04
- HQ: Paris / New York
- Type: ai-software
- Website: https://www.owkin.com

## Sources

- Official website: https://www.owkin.com

## Connected records

- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- technologies: [Digital pathology & AI](https://onco.cc/technologies/digital-pathology-ai/), [Digital twins and virtual control arms](https://onco.cc/technologies/digital-twins-trials/), [Federated learning and privacy-preserving AI](https://onco.cc/technologies/federated-learning-medical-ai/), [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/), [Phikon / Phikon-v2 (Owkin)](https://onco.cc/technologies/phikon/), [Spatial-omics-guided treatment selection](https://onco.cc/technologies/spatial-omics-guided-therapy/)
- ideas: [A federated learning consortium of cancer centres that jointly own the models](https://onco.cc/ideas/idea-fund-federated-learning-consortium/), [A pre-competitive consortium to train a shared multimodal cancer foundation model](https://onco.cc/ideas/idea-data-precompetitive-cancer-foundation-model/), [AI-first reading for high-volume common cancer diagnoses, pathologist for the exceptions](https://onco.cc/ideas/idea-acc-ai-first-pathology-common-cases/), [Federated training of pathology and radiology models across hospitals](https://onco.cc/ideas/idea-data-federated-learning-imaging/), [Patient-level multimodal foundation models for treatment selection](https://onco.cc/ideas/idea-multimodal-foundation-model/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Data silos](https://onco.cc/bottlenecks/b-data-silos/), [No one can predict who responds to immunotherapy](https://onco.cc/bottlenecks/b-immunotherapy-response/)
- companies: [GV (Google Ventures)](https://onco.cc/companies/gv/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/)

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JSON: https://onco.cc/api/v1/entities/owkin.json