Digital Twins in Personalized Surgery

Abstract

Digital twins represent a technological innovation with the potential to transform personalized surgery by integrating anatomical, physiological, and clinical data into dynamic virtual models capable of simulating individualized surgical scenarios. This review aimed to analyze the scientific evidence regarding the applications, benefits, limitations, and future perspectives of digital twins in personalized surgery. An integrative literature review was conducted according to the PRISMA 2020 guidelines. Searches were performed in PubMed/MEDLINE, Scopus, and Web of Science using descriptors related to digital twins and surgery. Eighteen studies were identified, of which six met the eligibility criteria and were included in the final analysis. The selected studies demonstrated that digital twins can be applied to preoperative planning, intraoperative guidance, postoperative monitoring, surgical training, and precision medicine. The main reported benefits include improved surgical accuracy, personalized interventions, reduced complications, and enhanced clinical decision-making. However, challenges related to clinical validation, standardization, interoperability, implementation costs, data security, and ethical issues still limit their widespread adoption. Digital twins have considerable potential to reshape surgical practice, although robust clinical studies and further technological advances are required before their routine implementation in healthcare systems.