AI-Powered Digital Twin for Personalised Healthcare in the NHS

Personalised healthcare is a strategic focus of the National Health Service (NHS), but the current clinical pathways are mostly reactive and population-focused. The advent of digital twin (DT) technology, together with advancements in artificial intelligence (AI), is a revolutionary way to individualised care. A digital twin powered by AI combines multimodal data, such as electronic health records, laboratory results, imaging, and wearable devices, into a dynamic and robust computational model which is constantly updated with the current state of the patient, as well as predicts future paths. In this paper, a framework to develop and assess an AI-powered digital twin in cardiovascular care in the NHS is outlined. It examines the architectural elements needed to be integrated with the existing NHS data infrastructures and FHIR-based interoperability standards, and the regulatory and governance standards needed. Digital twins can improve risk prediction and therapy optimisation for illnesses like heart failure and arrhythmias, that will be illustrated by a case study on the cardiovascular system. Additionally, we propose an assessment system that takes into account clinical results, technical precision, economic value, as well as ethical safeguards. In addition, implementation of the multi-layered clinical, technical, and economic dimensions in the NHS are presented in the form of a visual evaluation framework. This study shows how AI-focused digital twins could transform personalised healthcare by prioritising cutting-edge computational modelling and harmonising it with the priorities of the NHS to provide measurably better results, efficiency, and patient experience.