Digital twin-assisted surgery referred to the use of a dynamic, patient specific virtual model that mirrored physical patient in real time to enhance surgical planning, guidance, and outcomes. This emerging approach integrated data from robotic manipulators, intelligent implants, biosensors, and imaging systems to create a continuously updated digital representation that responded to physiological and procedural changes. This review provided a comprehensive analysis of the conceptual foundations and technological architecture that enabled digital twin functionality in surgical environments along with associated implementation challenges. It further explored real-world integration of digital twins across preoperative, intraoperative, and postoperative phases, highlighting their role in predictive modeling, real-time decision support, and personalized surgical recovery. Moreover, this review critically evaluated key implementation challenges, including technical hurdles (data interoperability, sensor accuracy, real-time processing), practical barriers (economic costs, infrastructure, and training), and broader concerns (regulatory standards, ethics, and global equity). To address these challenges, the review proposes targeted future directions including leveraging AI and edge computing to overcome technical hurdles like real-time processing, developing secure, standardized data frameworks to ensure interoperability and meet regulatory standards and, moreover, fostering public–private partnerships to solve economic, infrastructural, and training barriers while promoting global equity. As the field evolved, digital twin-assisted surgery is poised to become a cornerstone of precision surgical care.
Batool et al. (Tue,) studied this question.