Robust
Handles low-quality, real-world data where standard federated learning breaks.
RefinedAI
Robust AI for Distributed Low-quality Healthcare Data
AI for Real-World Data: Without Breaking Confidentiality
Unlock the power of your sensitive, low-quality, and distributed healthcare data with RefinedAI's privacy-first robust federated learning platform.
Animated concept of the RefinedAI monitoring dashboard: five clinical sites stream model updates over 1000 training steps, occasional low-quality updates are filtered across all sites, and shared-model accuracy rises and holds above 90 percent.
RefinedAI monitoring
training active
Sites connected
5
Model accuracy
8%
Updates filtered
0
Live training status
step 0 / 1000
Accuracy over training
dashed line = 90%
Rejected updates per site
What is RefinedAI?
Train powerful AI without moving patient data.
RefinedAI is a federated learning platform that lets healthcare centres train powerful AI models without ever centralizing their sensitive patient data.
Each site keeps its data local. Only model updates are shared. Robust aggregation automatically filters out low-quality or corrupted contributions before they can harm the shared model.
- Data never leaves the institution
- Only encrypted model updates are exchanged
- Corrupted or low-quality updates are filtered out automatically
Why choose RefinedAI?
Built for real-world healthcare data.
Research-backed
Built on a peer-reviewed algorithm published at ICML 2023, a top machine-learning conference.
Recognised by experts
The underlying method is cited in the 2025 US NIST report on trustworthy AI.
Privacy-first by design
Patient data never leaves the institution; GDPR and EU AI Act aligned.
How it works
From local data to a shared, privacy-safe model.
Local model training
Your data never leaves your site. Each institution trains a model locally on its own infrastructure.
Encrypted model aggregation
Only encrypted model updates are shared, never raw data, and securely combined across all participating sites.
Automatic data-quality checks
Built-in robust aggregation detects and filters noisy, biased, or corrupted updates before they can affect the shared model.
Deploy & scale
Deploy accurate, privacy-first robust AI models across your hospital or research network, and add new sites with ease.
Live platform demo
Animated concept of the RefinedAI monitoring dashboard: five clinical sites stream model updates over 1000 training steps, occasional low-quality updates are filtered across all sites, and shared-model accuracy rises and holds above 90 percent.
RefinedAI monitoring
training active
Sites connected
5
Model accuracy
8%
Updates filtered
0
Live training status
step 0 / 1000
Accuracy over training
dashed line = 90%
Rejected updates per site
Research & credibility
Peer-reviewed science, recognised by regulators.
Peer-reviewed algorithm at a top ML conference
RefinedAI is built on a federated learning method published at the International Conference on Machine Learning (ICML) 2023 — one of the world's leading venues for machine-learning research.
Cited in the US NIST report on trustworthy AI
The underlying robust aggregation method is referenced in the 2025 NIST report on adversarial machine learning and trustworthy AI systems.
Join our pilot
We are inviting 3–4 hospitals and research centres
We are inviting 3–4 hospitals and research centres
to join our pilot programme.
What partners get
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A working AI system, at no cost
A fully functional federated-learning platform, deployed at your institution.
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Your data never leaves your servers
Patient records stay entirely on-site, fully GDPR-compliant, at every step.
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A co-authored validation study
Publishable results, with your team as collaborators.
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A direct hand in the product
Your feedback shapes how RefinedAI develops.
Your data stays on-site while we install lightweight client software. Participating institutions then collaboratively train a shared model on a clinical task you choose, such as diagnostic imaging, risk prediction, or lab-result analysis, without ever exchanging patient data.