The AI Centre are developing a range of technologies and platforms, to enable better identification of diseases early, more accurate diagnosis and personalised treatment.

These platforms support the full lifecycle of AI development, taking models from conception, to development and deployment, within the clinical environment.

FLIP: Federated Learning Interoperability Platform

FLIP is an open-source platform (https://github.com/londonaicentre/FLIP) that links data from multiple NHS Trusts to enable AI at scale. It includes secure data storage for processing and analysis within each of our partner NHS Trusts – a secure enclave or area within the firewall that keeps sensitive patient data inside the Trust.

FLIP comprises three main parts, outlined below.

Secure Enclaves

We are building dedicated secure data storage for processing and analysis within each of our partner NHS Trusts – a secure enclave or area within the firewall that keeps sensitive patient data inside the Trust.

Data from across the Trusts’ patient records systems will be transfered into the secure enclave for curating and aggregation, unifying medical imaging scans from Picture Archiving and Communications Systems (PACS, the industry-standard storage system incorporating different medical imaging types) and other electronic health data.

Interoperability and data harmonisation

Electronic healthcare records are complex and heterogeneous, making it difficult to create interoperable algorithms that can be applied to data that is stored and categorised in different ways across multiple sites. 

To harmonise this heterogeneous data, we use ontological and data interoperability
standards to structure the data and make it actionable. When data is standardised and harmonised across multiple hospitals and clinical systems, it is possible for AI algorithms to query, learn and action data via an open standards-based data interface. This consistency makes interoperability possible, allowing researchers to extract valuable insights from multiple data sources.

Federated learning and evaluation

Our federated learning approach brings algorithms to the data within each NHS Trust’s secure enclave, without needing to share information outside the secure firewall or break local governance rules. 

Algorithmic models are sent to multiple Trusts and trained on local data before being securely combined to achieve consensus. The model is then applied within each secure enclave, where it learns from the data, is updated again, and the process repeated until an improved consensus model is created. 

Read more about FLIP in its official documentation.

Cogstack

Cogstack is an ‘in-house’ spin out from KCL, KCH, UCLH, SLaM and GSTT had been operating in our hospitals for a number of years. Its platform uses Natural Language Processing (NLP) to extract concepts from medical records and Machine Learning models (MedCat) to speed up diagnosis, treatment options, enhance coding and improve data quality.

deepcOS

deepc is an external company that provide an AI deployment platform, deepcOS, which synchronises radiology and radiotherapy applications in a wide range of clinical pathways. It orchestrates data flows and predictions, and provides platform for monitoring, across radiology AI applications.

The AI Centre and deepc entered a collaboration agreement on use and future developments of the platform. NHS trusts are also using the deepc platform to deploy in-house AI models in radiology and radiotherapy, such as AutoSegCT: a radiotherapy organs-at-risk contouring AI application.