The key to becoming a medical specialist, in any discipline, is experience.
Knowing how to interpret symptoms, which move to make next in critical situations, and which treatment to provide — it all comes down to the training you’ve had and the opportunities you’ve had to apply it.
For AI algorithms, experience comes in the form of large, varied, high-quality datasets. But such datasets have traditionally proved hard to come by, especially in the area of healthcare.
Medical institutions have had to rely on their own data sources, which can be biased by, for example, patient demographics, the instruments used or clinical specializations. Or they’ve needed to pool data from other institutions to gather all of the information they need.
Federated learning makes it possible for AI algorithms to gain experience from a vast range of data located at different sites.
The approach enables several organizations to collaborate on the development of models, but without needing to directly share sensitive clinical data with each other.
Over the course of several training iterations the shared models get exposed to a significantly wider range of data than what any single organization possesses in-house...
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