https://bura.brunel.ac.uk/handle/2438/8641 Mon, 19 Oct 2026 00:16:33 GMT 2026-10-19T00:16:33Z https://bura.brunel.ac.uk/handle/2438/33916 Title: Healthcare AI risk management: an innovator-informed approach from frameworks to practical worksheets for proportionate regulatory evidence capture Authors: Zebin, Tahmina; Wu, Jinyan; Colecchia, Federico; Shinhmar, Sonia; Bondaronek, Paulina; Potts, Henry WW; Tucker, Allan; Spinelli, Gabriella Abstract: Introduction: Artificial intelligence (AI)-enabled healthcare systems introduce clinical safety, operational and governance risks across development, deployment and post deployment stages. Although multiple AI risk assessment frameworks exist, they are often cross-sector, lengthy and difficult to operationalise in ways that support proportionate evidence generation and regulatory use. Methods: We conducted a targeted evidence synthesis and framework mapping exercise drawing on review-level evidence, scientific databases and regulatory sources. Three core review studies informed framework identification and mapping. Selected frameworks and guidance, including NIST AI RMF, the Fraunhofer IAIS AI Assessment Catalogue, ALTAI, the ICO AI and Data Protection Risk Toolkit, MHRA guidance, the NICE Evidence Standards Framework, SPIRIT-AI and CONSORT-AI, were mapped across lifecycle stages using a structured extraction matrix. Framework concepts were translated into healthcare-oriented worksheets and refined through structured engagement with healthcare AI innovators. Results: Frameworks varied substantially in scope, lifecycle coverage, terminology and implementation approach. Innovator feedback identified recurring themes including cognitive burden, usability, need for clearer intended purpose, lifecycle support, actionable examples and improved regulatory traceability. Participants highlighted challenges applying frameworks across diverse healthcare contexts and AI roles. Worksheet refinement introduced simplified domains, healthcare-specific context prompts, evidence and action fields and stage-aware guidance. Discussion: AI risk frameworks are necessary but not sufficient for healthcare innovation unless translated into practical tools. A codesigned worksheet approach can support proportionate evidence capture and iterative risk management while remaining adaptable across use cases and maturity levels. Description: Data availability statement: All data relevant to the study are included in the article or uploaded as supplementary information. The framework mapping and worksheet development are fully described in the manuscript; example worksheet templates are available from the RADIANTCERSI’s official website. The underlying stakeholder consultation data are not publicly available because they contain potentially identifiable or confidential information. Mon, 24 Aug 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33916 2026-08-24T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33889 Title: The Influence of Impressionism on Contemporary Poster Design: A Review and Comparative Insights from the UK and China Authors: Yin, Haowen; Garaj, Vanja Abstract: ... Description: ... Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33889 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33888 Title: A Slide-Rail Electric Vehicle Charging System for Commercial Indoor Parking: The Conceptual Design and Simulation-Based Evaluation Authors: Huang, Zeqi; Hou, Boteng; Qin, Jiayue; Garaj, Vanja Abstract: ... Description: ... Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33888 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33874 Title: Towards Engram Memory Machines. A Feasibility Study of Image Classification Using Warping and Low-dimensional Class Prototypes Authors: Colecchia, Federico Abstract: Against the backdrop of ongoing green AI research to reduce the environmental footprint of artificial intelligence technology, a new supervised machine learning approach is presented with potential to contribute to the development of artificial systems capable of learning incrementally from new data. This article reports results from a feasibility study on image classification using the Modified National Institute of Standards and Technology data set. Training consists of producing class-level image prototypes, referred to as engrams, using a single-pass method. At inference stage, previously unseen images are warped to match the engrams individually, using a continuous transformation including a tuneable rigidity parameter. The images are classified by maximising similarity to the engrams across classes following warping. Unlike alternative approaches recording binary feature coincidences in highdimensional spaces, this method operates on low-dimensional data representations. In addition to enhancing interpretability, this has the advantage of circumventing memory scaling bottlenecks. A test accuracy above 90% was observed in this study, which is a promising result considering that the engrams exclusively encoded average data patterns. Future work will focus on reducing inference time, enhancing classification accuracy and estimating the performance of the method on additional datasets. Sat, 15 Aug 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33874 2026-08-15T00:00:00Z