The authors searched seven databases, including PubMed/MEDLINE, Scopus, Web of Science, CINAHL, Embase, IEEE Xplore and Google Scholar, for English-language literature on digital twins, computational cardiovascular modelling, artificial intelligence-assisted cardiovascular care, wearable technologies, remote monitoring systems and nursing implications. The screening process began with 742 studies identified through database searching, plus 18 more found through manual reference searching. After removal of 126 duplicates, 634 records underwent title and abstract screening, of which 521 were excluded. Full-text assessment covered 113 studies, and 83 were excluded for lacking specific relevance to cardiovascular digital twin systems, nursing implications or clinical applications, leaving thirty included studies.

The review found that digital twin technology is increasingly applied in heart failure monitoring, arrhythmia prediction, telecardiology, smart intensive care systems, virtual cardiac modelling, personalised rehabilitation and population-level cardiovascular risk prediction. Artificial intelligence and machine learning were the most frequently reported technological components, used mainly for predictive analytics, risk stratification, computational cardiac modelling and clinical decision support. Wearable biosensors, remote cardiac monitoring devices, mobile health applications, Internet of Medical Things systems, cloud computing platforms and electronic health record integration were also commonly identified, alongside electrophysiological simulations, virtual physiological models, finite element modelling and computational haemodynamic modelling.

Early detection and risk prediction emerged as one of the most extensively reported applications. The reviewed literature described digital twin systems that continuously analyse physiological parameters to identify subtle signs of deterioration before overt clinical symptoms appear. Heart failure deterioration prediction was frequently discussed because of the condition’s chronic, progressive course and its association with recurrent hospital admissions. Studies reported that wearable biosensors, remote monitoring devices and AI-supported predictive analytics could identify changes in heart rate variability, oxygen saturation, blood pressure, respiratory status and fluid balance, potentially facilitating earlier intervention and reducing avoidable hospitalisation. Computational cardiac models and electrophysiological simulations were also used to predict atrial fibrillation recurrence, ventricular tachycardia and other rhythm abnormalities through analysis of imaging data and real-time electrocardiographic signals.

Personalised treatment planning formed a second major application area. Patient-specific virtual models could simulate individual responses to medications, interventions and rehabilitation programmes before implementation in clinical practice. In precision cardiology, digital twin systems were reported to assist clinicians in selecting personalised pharmacological strategies by predicting haemodynamic responses, identifying potential adverse effects and analysing patient-specific physiological data. These approaches were described as particularly relevant for arrhythmia management, catheter ablation procedures and structural heart interventions, where digital simulations helped optimise therapeutic planning and procedural precision. Tailored rehabilitation planning was another reported use, with wearable monitoring systems and digital rehabilitation platforms enabling clinicians to individualise exercise programmes according to patients’ cardiovascular tolerance, physiological responses and recovery progress.

Remote cardiac monitoring represented what the review called one of the most clinically applicable uses of the technology. Studies reported increasing integration of telecardiology systems, wearable biosensors, mobile applications and Internet of Medical Things technologies into cardiovascular care pathways. Home-based monitoring systems facilitated continuous assessment of electrocardiographic recordings, oxygen saturation, blood pressure and activity levels, which the included studies suggested improved continuity of care and reduced unnecessary hospital visits. Real-time data transmission allowed clinicians and nurses to respond rapidly to abnormal physiological changes and optimise patient management remotely. In critical care, smart ICU systems integrating predictive analytics, AI-supported alerts and haemodynamic simulations were identified as emerging innovations, with predictive systems reported to assist healthcare professionals in identifying arrhythmias, respiratory compromise and haemodynamic instability before clinical deterioration became severe.

The review also mapped implications for cardiovascular nursing, while cautioning that these remain emerging rather than established. The authors described a potential transition from reactive nursing care toward predictive and preventive practice, in which nurses interpret digital health data, monitor predictive alerts and participate in AI-assisted patient management. Nurse-led remote monitoring and telehealth integration were highlighted as important areas, enabling virtual follow-up, remote physiological monitoring and coordination of chronic disease management, with virtual nursing models considered particularly valuable for patients in rural and underserved regions with limited access to specialist cardiovascular services. Patient education remained a central nursing responsibility, with studies reporting that digital health coaching improved patient engagement, medication adherence, lifestyle modification and self-management behaviours. The review also emphasised a growing need for digital competency, including skills in AI interpretation, telehealth systems, wearable technologies and digital health analytics, and recommended integrating digital health education into undergraduate nursing curricula and continuing professional education.

Significant barriers accompanied these opportunities. The reviewed studies consistently highlighted ethical and legal concerns, with data privacy and confidentiality described as among the most important challenges because digital twin systems require continuous collection, integration and storage of large volumes of sensitive patient information. Questions were raised about informed consent, data ownership and whether patients fully understand how their data are used for modelling, simulation and machine learning. Algorithm bias was another concern: AI systems trained on limited or non-diverse datasets may generate biased predictions and contribute to inequitable healthcare outcomes, particularly among underrepresented populations. Technological and organisational barriers included high infrastructure and implementation costs, interoperability problems between incompatible electronic health record systems and devices, and cybersecurity risks arising from continuous data exchange between wearables, cloud platforms and hospital information systems. Workforce preparedness was repeatedly identified, with many healthcare professionals, including nurses, reported to lack sufficient training in digital health technologies, AI systems, predictive analytics and telehealth platforms, alongside resistance to technological adoption among some staff and patients.

The review also documented pronounced geographic and evidentiary limitations. Most included studies originated from technologically advanced healthcare systems in North America and Europe, with the United States contributing the largest number, followed by the United Kingdom, Sweden, Italy, Japan, Australia, Germany, Canada, Spain, South Korea and multinational European collaborations. Limited evidence was identified from low- and middle-income countries, where inadequate internet connectivity, insufficient access to advanced technologies and lack of specialised computational resources were reported as major barriers, raising concerns that unequal technological access may widen healthcare disparities. Narrative reviews, conceptual reviews and computational modelling studies represented the largest proportion of included studies, and only a limited number evaluated real-world clinical implementation or longitudinal cardiovascular outcomes. Heart failure was the most frequently discussed cardiovascular condition, with arrhythmias, coronary artery disease, hypertension, hypertrophic cardiomyopathy, myocardial infarction and structural heart disease also commonly addressed.

The review’s authors were explicit about the strength of its conclusions. Because most included studies were conceptual reviews, modelling studies or theoretical discussions rather than real-world clinical evaluations, the available evidence on long-term clinical effectiveness, patient outcomes and cost-effectiveness remains limited, and the identified nursing implications should be interpreted as emerging applications rather than established nursing interventions. Formal methodological quality assessment was not undertaken, consistent with the exploratory purpose of a scoping review, and the inclusion of only English-language publications may have excluded relevant studies. The authors concluded that future research should prioritise longitudinal clinical studies to establish effectiveness and patient outcomes, alongside ethical and regulatory framework development, cost-effectiveness evaluations and nursing-led implementation research, before digital twin technology can be translated safely and equitably into routine cardiovascular practice.

Subject of Research: Applications of digital twin technology in cardiovascular care and its implications for cardiovascular nursing practice

Article Title: Digital twin technology in cardiovascular care: future perspectives for nursing practice, patient management, and clinical decision-making

Article References: Shafqat, N., Verma, V., & Naseema, S. (2026). Digital twin technology in cardiovascular care: future perspectives for nursing practice, patient management, and clinical decision-making. Frontiers in Cardiovascular Medicine, 13, 1883153. https://doi.org/10.3389/fcvm.2026.1883153

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1883153

Keywords: digital twin technology, cardiovascular care, cardiovascular nursing, artificial intelligence, wearable biosensors, remote patient monitoring, predictive analytics, precision cardiology, telecardiology, heart failure, arrhythmia prediction, scoping review