FLock.io spotlighted by the WEF for NHS AI use cases (4 Aug)

Covent Garden, UK, August 4th, 2026, Chainwire FLock.io has been spotlighted by the World Economic Forum (WEF)’s MINDS programme for two NHS trusts using its privacy-preserving AI to tackle major diseases. Both trusts use its federated learning platform to train clinical models while maintaining 100% data sovereignty. Moorfields Eye Hospital and UCLH are using FLock.io for two use cases: eye disease detection and diabetes management. The method enables collaboration without sharing sensitive patient data. This solves the problem regulated industries like healthcare face where data privacy regulations and security concerns restrict the use of AI. The spotlight places FLock.io’s work within the wider MINDS programme, alongside a broader ecosystem focused on scaling high-impact, real-world AI applications in collaboration with Accenture. The latest MINDS cohort includes organisations such as Lenovo, Occidental, TCL Industries, Hisense Hitachi and KUKA. Two federated learning NHS use cases with FLock.io FLock.io is working with NHS researchers from UCL and clinical partners from University College London Hospitals (UCLH) for glucose monitoring alerts. This empowers clinicians with AI-powered predictions locally trained on 400+ patients’ data. It enables collaborative training across partners in the UK, Europe, US, and China while ensuring patient data never leaves the secure NHS trust network, maintaining 100% data sovereignty. Approximately 14,000 end users, including patients using diabetes management apps, engage with FLock.io’s platform across the UK, Southeast Asia and East Asia. The next phase – a multi-continental glucose prediction real-world trial with 100 patients – will begin this summer. FLock.io estimates that AI-driven prevention in the NHS could result in over £100M in annual savings, based on a 1% reduction in the £10B+ currently spent on diabetes management. With Moorfields Eye Hospital, FLock.io has completed the initial research for federated eye disease detection. Training of the AI model using the hospital’s image data is underway. It aims to solve scaling issues that traditional centralised AI could not, and allows for multi-site training across NHS trusts without requiring them to share sensitive imaging data externally. The long-term goal is to replicate these models across additional NHS trusts. The NHS’ single-payer system and consistent data governance make it ideal for proving federated learning at scale before expanding to other markets. Federated learning allows collaborative AI model training without sharing raw data. Each participant trains the model locally and securely on-premises or on edge devices. They share only encrypted model updates, which are then aggregated to improve the model’s performance, enabling real-time inference. The problem FLock.io aims to solve Data privacy regulations and security concerns restrict the use of AI by regulated industries holding sensitive data, including hospitals, banks and governmental agencies. It forces organisations to either forgo AI adoption or rely on generic models that lack domain accuracy or introduce compliance risk.