Analysis of digital twin applications in nursing practice and education: a scoping review

This scoping review examined research applying digital twins in nursing practice and education and summarized their application domains, methods, outcomes, and implications. A human digital twin is a virtual health replica modeled from real-world data. This study followed the 5-stage scoping review process proposed by Arksey and O'Malley. Two researchers independently conducted the literature search without restrictions on publication year. From April 1 to 15, 2026, the Cochrane Library, PubMed, Embase, CINAHL, ERIC, and RISS databases were searched, and 15 studies were ultimately included. Digital twin applications were identified in 3 major domains: clinical practice and patient-centered care, education and training, and decision-making and workflow management. Application methods and outcomes varied according to technological implementation and included (1) modeling and data-driven prediction, (2) development of immersive learning and practice-training environments, and (3) system integration and decision-support frameworks. In clinical settings, multimodal patient data can be analyzed using artificial intelligence and machine learning to generate a virtual persona resembling the patient, thereby facilitating real-time personalized nursing care and self-management. In educational settings, digital twins can provide realistic and safe learning environments that enhance training effectiveness. Digital twins show substantial potential to advance predictive and personalized nursing in both clinical practice and education. Their data-driven capabilities are expected to contribute to innovative applications in future nursing practice and educational environments.