<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/5E88B159-F6E9-4B24-8208-1E728B3BCCFC" ns1:id="5E88B159-F6E9-4B24-8208-1E728B3BCCFC"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/448EAF7A-F43E-46C6-A7BE-CCE5E9890D6C" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F739D642-26BA-4B54-8D13-A40E80D219F9" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F739D642-26BA-4B54-8D13-A40E80D219F9" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-07-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/45DDCBE7-22A5-457F-B1CB-EE6BB8E80368" ns1:rel="FUND" ns1:start="2023-04-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10065901</ns2:identifier></ns2:identifiers><ns2:title>Delivering large scale digital clinical trials for artificial intelligence</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>The project aims to speed up the deployment, evaluation, and commissioning of AI in healthcare by creating a near real-time evaluation toolkit for AI applications. The cost of clinical trials for AI applications can be prohibitively expensive for small and medium-sized businesses, making it difficult for them to prove the safety and efficacy of their products.

To address this issue, the project has three main goals: to determine the appropriate performance metrics for identifying safe and trustworthy AI applications in healthcare, to gather feedback from clinical users in near real-time without interrupting their workflows, and to generate evidence of efficacy at a large scale while maintaining data privacy.

The project will use mixed methods research and a privacy-preserving federated AI software evaluation toolkit to achieve these goals, and once completed, will be used to evaluate AI at a large scale with the help of SMEs, healthcare providers, and commissioners.</ns2:abstractText></ns2:project>