We blend human insight with safe and fast data access to train high performance models.
We combine cutting-edge technologies, like federated learning, with critical clinical expertise and a network of academic researchers. This accelerates research in a safe and privacy-preserving way.
Challenges
What if we could share global data insights seamlessly?
Strict regulation to protect patient data
Regulatory requirements such as GDPR and HIPAA and a fear of compromising competitive assets or patient privacy makes data sharing complex.
Disjointed data
Complementary datasets cannot be easily accessed and combined for model training.
Data harmonization
Differing standards of data structure, digitization, storage, privacy compliance, and geographical distribution stack up and compound each other.
Strict regulation to protect patient data
Regulatory requirements such as GDPR and HIPAA and a fear of compromising competitive assets or patient privacy makes data sharing complex.
Disjointed data
Complementary datasets cannot be easily accessed and combined for model training.
Data harmonization
Differing standards of data structure, digitization, storage, privacy compliance, and geographical distribution stack up and compound each other.
Owkin’s solution
We unite Medical, IT and Data Science experts with groundbreaking technologies in our secure network to overcome these challenges for faster and more effective research.
Substra (formerly Owkin Connect)
Substra (formerly Owkin Connect)
Expand access to health data while protecting privacy with Substra, open source federated learning software developed by Owkin
Substra technology powers collaboration in healthcare by ensuring data confidentiality and compliance.
Substra for clinical research
The HealthChain project unites AI experts and medical researchers to foster medical innovation in anatomo-pathology, dermato-oncology and fertility while preserving patients' privacy. Requirements such as GDPR and HIPAA and a fear of compromising competitive assets or patient privacy makes data sharing complex.
BioPharma companies and healthcare institutions deploy Substra in various settings for clinical research and drug discovery.
Substra for drug discovery
The MELLODDY Consortium tackles the $1.3Bn challenge of bringing a new drug to market. This project enhances predictive machine learning models on the decentralized data of 10 pharmaceutical companies, without exposing proprietary information.
Consortia
Consortia
Accelerate your breakthrough research by building collaborative projects in a privacy-preserving way with federated learning.
Free up resources from data pooling and management work packages to focus on what really matters: your scientific question. Our decentralized-AI technology lets you set up secure and efficient consortia: data is never shared between partners; only the encrypted models travel between the data centers.
Data & KOL network
Data & KOL network
Solve the biggest questions in cancer by combining the very best biomedical expertise with artificial intelligence. Owkin has built a world-class research network of leading academic centers, KOLs and expertly-curated, high-quality patient data.
Owkin’s data network contains cohorts of rich longitudinal datasets that medical experts have curated for fit-for-purpose standard and AI analysis. They span many modalities; omics, imaging, histology, and clinical data, comprehensively capturing oncology’s heterogeneous nature.
Testimonial
“In terms of security, Owkin provided the methodology, as well as the external audits which, for us, are a guarantee of security. Owkin’s philosophy of excellence and speed is closely aligned to that of cancer centers – there’s a great cultural fit. Today, if we wanted to apply AI to our data, I wouldn’t think twice – I’d call Owkin.”
Head of IT Systems at Centre Léon Bérard
Federated learning
Federated learning
Connect disjointed data to build accurate models without sharing or pooling data with federated learning
Unlocking access
Federated Learning powers precision medicine by unlocking access to the right quality and quantity of data to train high-performance models.
Data stays local
With Federated Learning the data stays local - only models and insights travel between the servers, protecting privacy and ensuring compliance.
More accurate models
This technology allows research communities to benefit from access to more datasets, training on heterogenous data to generate more robust and accurate models.
Take a closer look
