In this collaborative proposal, the Aival Analysis Lab will be used to evaluate commercial radiology AI products on imaging data representative of a broad Scottish population. We will consider two clinical treatment pathways in line with NHS Scotland service and procurement priorities: stroke triage and urgent suspicion of lung cancer triage, assessing at least three commercial products in each case. Furthermore, we will develop a fully integrated platform capable of monitoring the performance of AI products in deployment at clinical sites. The use of AI in healthcare can lead to improved outcomes and increased efficiency in healthcare systems. However, AI models are known to produce variable results when applied to different demographics or when changes are made to a given clinical workflow, causing their accuracy to decrease and reducing operator trust. Our AI model evaluation and monitoring tool provides a way of assessing whether AI models are operating as designed in new deployments. This then accelerates integration into new clinical sites as well as confirming that the desired performance is both initially achieved, and maintained over time. Ensuring that AI models behave as expected means that overall operational costs are reduced for both the vendor and healthcare user as any deviations in performance are found and addressed quickly. Our software provides the ability to assess the performance of different AI models on a given patient population. It can be deployed locally at any clinical site and used to report evidence of performance, fairness and robustness on data from local populations, operators and workflows. We report metrics across subgroups of the population, ensuring that benefits and risks are equally distributed. Our software can explain the reasoning behind the decisions of black-box AI models, providing reports with visual outputs that make this clear and accessible to users. This explainability enables clinical users to gain trust in the outputs of an AI model. Clinical sites also often wish to benchmark different solutions for a given disease indication against one another to determine which is most suitable for their patient population and use case. Our tool provides the functionality to directly generate reports enabling this comparison. The software is light, scalable and easy to integrate. In this project we aim to prove its efficacy in assessing and monitoring AI model performance and its ability to accelerate AI model deployment and reduce costs.

This project will develop a range of novel polymer additives that are able to downconvert UV light to other parts of the electomagnetic spectrum, by incorporating phosphor particles into the polymers. By down converting the UV and visible (blue and green) light into red and near infra -red, these materials will be longer lasting (not be degraded by UV light). This generic technology will have a huge range of industrial applications generating large market share for UK industry, including car side & roof windows, conservatory windows & roofing, stadium roofing and thermal insulation.