Digital twin applications in adult critical care: A scoping review of current development and implementation trends

Digital twins (DTs) show promise in critical care by enabling personalised treatment and optimising clinical decision-making. Despite the complexity and data-intensive nature of critical care, the implementation of DTs in this setting remains under-investigated. This scoping review aimed to summarise DT research in critical care and identify current evidence gaps.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, seven electronic databases were searched. Studies reporting the development or evaluation of DT models in adult critical care were included. Data were extracted on study characteristics and DT development features, including modelling approaches, levels of data integration, and key findings.

Twenty-three studies were included, with most originating from North America and Europe. Retrospective designs using hospital datasets derived from intensive care unit and emergency department settings were common. Data integration predominantly corresponded to the digital model level of the DT maturity, whereas fully automated DT implementations were rare. Regarding modelling approaches, mathematical models were most frequently developed, followed by machine learning-based predictive models. DT application primarily focused on predictive modelling and virtual patient simulations to enhance personalised treatment, support clinical decision-making, and optimise organisational resource allocation.

DT technologies in critical care remain in the exploratory and early stages of development and implementation. Further research incorporating higher levels of data integration, real-time deployment, and longitudinal external validation is warranted, alongside broader consensus on ethical governance and data privacy.