Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in ph...Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.展开更多
文摘Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.
