Federated Learning Can Aid Clinical Research Says Rhino Health

By
The Rhino Team
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January 17, 2023

Federated learning (FL) is an AI technique that allows predictive models to be trained across multiple decentralized data sources without the need to centralize data. This approach enhances patient privacy and reduces the risk of data breaches, as data remains within its original location. In clinical research, FL facilitates the use of large, diverse datasets from various hospitals and clinics, improving trial site selection, speeding up patient recruitment, and enhancing remote monitoring capabilities. Additionally, by processing data locally, FL reduces the need for powerful centralized servers, potentially lowering computing costs. Despite its advantages, the adoption of FL in clinical trials has been limited, partly due to the lack of analytical infrastructure. However, companies like Rhino Health are collaborating with contract research organizations (CROs) and biopharma clients to implement federated learning frameworks, aiming to unlock the full potential of this technology in clinical research.

For more insights, read the full article on Informa Connect.

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