A federated learning consortium of cancer centres that jointly own the models
Hospitals could train shared AI models on all their patients' scans and records without any data leaving the building, and jointly own the results, if someone built and governed the network.
A consortium of cancer centres and trial groups operating a federated learning infrastructure (models travel, data stay) for pathology, radiology and multimodal outcome prediction, with a governance agreement that gives members joint ownership of trained models, shared validation protocols and a route to regulatory submission. Owkin's federated networks and the EU's several federated health data projects show technical feasibility; the missing piece is a durable, member-owned institution with IP terms that reward data contribution rather than data extraction. Prospective validation of consortium models in member trials closes the loop with the AI validation bottleneck.
- Secrecy and intellectual property block collaboration · Companies with complementary drugs rarely test them together, and data that could answer questions stays locked up.
- Data silos · Records, scans, genomes and outcomes sit in separate systems that cannot talk. Every patient's experience is lost to the next.
- AI that is built but not validated or deployed · Thousands of cancer AI models are published; a handful are in clinical use, and fewer have shown they help patients.
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