Our Platforms

Healthcare AI requires state-of-the-art technology to achieve the best results for clinicians and patients.

At the AI Centre, we are developing technology that allows us to access large volumes of well-curated clinical data in a controlled environment, with the appropriate technical architecture, governance, and clinical expertise.

Using the MONAI framework to access the latest AI and machine learning models, we are creating applications that will drive innovation in healthcare and allow for the fundamental redesign of clinical pathways to improve outcomes and reduce costs.

We are developing two platforms, AIDE and FLIP, which will provide access to high-quality electronic health data for the purpose of development and deployment of AI Technology. They will enable NHS trusts to test and deploy AI in clinical practice and share data in a way that is safe and secure.

AIDE - AI Deployment Engine
Radiology is fundamental to the diagnostic process with approximately 133,000 scans produced every day in the UK, but the NHS faces a 29% shortfall of clinical radiologists to analyse those images. This has direct consequences for patient outcomes since any delay in radiology imaging has been shown to have a negative impact on patient outcomes including a longer stay in hospital and an increased risk of severe
complications.
Radiology AI has the potential to relieve these backlogs by increasing the number of scans radiologists can analyse while improving the accuracy of the diagnosis.There are now over 1000 clinically proven radiology AI solutions available to health systems and organisations globally covering a wide variety of use cases. However, few NHS patients are currently benefiting from these advances because deploying radiology AI solutions in clinical settings remains expensive, time-consuming and technically complex, particularly when dealing with legacy IT systems.
To overcome these barriers we developed AIDE: AI Deployment Engine -an intelligent tool that allows healthcare providers to deploy AI models safely, effectively, and efficiently by enabling the integration of AI models into clinical workflows.
We have subsequently partnered with deepcOS as a strategic partner, to collaborate with the AI Centre in developing its long-term aims by bridging the gap between cutting-edge AI innovation and clinical practice.
deepcOS has a strong track record of enabling the deployment and scalability of radiology AI solutions. Its platform is available across more than 40 countries and used by leading organisations. Its unified, scalable platform has been built to allow health systems to benefit from the rapid innovation advancing within radiology AI through a single integration that covers the data privacy, security, regulatory, procurement, integration, deployment, and operational challenges that can often delay the adoption of such new technologies.
By making it quicker, easier and cheaper for participating NHS Trusts to access and incorporate state-of-the-art radiology AI tools via a single platform into existing workflows, - deepcOS - AI portfolio. This will help to reduce waiting times for patients, enable clinicians to reach the right diagnosis faster and help relieve the pressure on radiology departments within the NHS.
deepc will implement deepcOS®, its industry-leading operating system, giving radiology departments access to more than 75 regulatory-approved AI solutions within a single platform. deepc will start rolling out the platform to initial NHS sites immediately.
Longer term, the partnership between deepc and the AI4VBH will also include collaborations to continue providing a safe and secure independent platform to facilitate other organisations and start-ups working to develop the next generation of AI tools for healthcare.
FLIP - Federated Learning Interoperability Platform
Complex and predictive patterns can be extracted from large volumes of data to train and validate AI algorithms and ensure they are safe for use in clinical practice.
Keeping the data inside the Trust firewall - a secure network that protects privacy - is the safest and most effective way to train AI in public health.
Our Federated Learning Interoperability Platform (FLIP) will help link data from multiple NHS Trusts to enable AI at scale. It is comprised of three parts:
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.
Federated Learning Interoperability Platform
The secure enclave is comprised of multiple hardware and software components.
We use high-performance NVIDIA DGX 1 processers to train our algorithms, which have the computing power to analyse hundreds of thousands of medical imaging scans in minutes.
XNAT, an open-source image informatics platform that ingests Digital Imaging and Communications in Medicine (DICOM, the industry-standard for medical images and related data) images from PACS, allows us to run our algorithms against automatically anonymised images safely.
CogStack is used for information retrieval and extraction. The platform can process data from multiple sources and applies Natural Language Processing to make sense of unstructured data and extract data from semantic information. We use CogStack to select participant cohorts for our research. CogStack builds on Elastic Search, for storage and complex queries, while Kibana is the front-end tool clinicians will use.
A secure enclave has been created at partner sites including King’s College Hospital Foundation Trust and Guy’s and St Thomas’ NHS Foundation Trust.
Federated learning
AI algorithms need to train on diverse clinical datasets from multiple sources to ensure the resulting model is both reliable and generalizable.
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.
To achieve convergence, the process of learning and combining is reiterated, until each locally applied model reaches the same conclusion, indicating that the model is generalizable and can be consistently applied.
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.
How FLIP adds value
Our Federated Learning Interoperability Platform (FLIP) ensures a high level of fidelity in AI output models compared to traditional aggregative data strategies because the data it trains on does not need to be anonymized before use.
FLIP also allows us to adhere to each Trust’s governance and data privacy regulations and ensures that our models are scalable in international contexts in full compliance with international laws and guidance.
FLIP will be deployed across seven NHS Trusts.

AI Centre for Value Based Healthcare

Our Platforms

Healthcare AI requires state-of-the-art technology to achieve the best results for clinicians and patients.

AIDE - AI Deployment Engine