Digital twins in cardiovascular disease: a scoping review.

Abstract

Digital twin technology in healthcare is an emerging approach that creates virtual representations of patients and disease-specific conditions, with the potential to clarify treatment objectives and enable more personalized, precision-based care, to help to clarify treatment objectives and to facilitate personalised and precision treatment management. This scoping review was conducted to analyse the application of digital twin technology in cardiovascular disease, focusing on implementation steps, clinical applications, and challenges to guide future research. A systematic search was conducted in eight databases (PubMed, EBSCO, Web of Science, WILEY, China WanFang Database, China National Knowledge Infrastructure, China Weipu Database, and SinoMed) for studies published, with a time frame of database construction to May 2025. Data were summarised and analysed based on predefined criteria. A total of 31 cardiovascular studies were included. Their implementation was categorised into five stages: data acquisition, model construction and personalisation, model calibration and validation, simulation analysis, and result application for decision support or medical education. Clinical applications involved personalised health management (13 %), precise individual treatment effects (42 %), individual risk prediction (26 %), clinical trial optimisation (23 %), and medical education (3 %). Key challenges included data limitations, model construction and validation complexities, and barriers to clinical application. Digital twins demonstrate potential in cardiovascular care by advancing personalised health management and precision medicine. However, their widespread adoption and practical implementation are still in their early stages. Broader implementation necessitates improved data sharing, algorithm optimisation, enhanced model generalizability, and ethical safeguards.