# Privacy-preserving Healthcare Beyond our work in the Web3 ecosystem, FLock is equally relevant in the Web2 world, and our flagship collaboration with a leading British hospital affiliated with a Top UK university is an excellent testimony to this. One of the biggest challenges of building AI models in the healthcare sector is data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausted, costly, and sometimes impossible. \ FLock’s groundbreaking approach in leveraging blockchain-enabled FL has proven to be effective in preserving data privacy while significantly improving prediction accuracy compared to traditional methods. This approach facilitates global collaboration, allowing healthcare institutions to contribute to model training without sharing sensitive healthcare data directly. Our work has been tested in blood glucose prediction as a pilot but can be readily extended to model other chronic diseases or address broader healthcare challenges. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.flock.io/flock-use-cases/privacy-preserving-healthcare.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.