Digital twin-enabled robotic surgery: a bibliometric and knowledge-mapping analysis from patient-specific simulation...

Digital twin-enabled robotic surgery is emerging at the intersection of surgical robotics, patient-specific simulation, artificial intelligence, extended reality, teleoperation, and surgical autonomy. However, its global research structure and translational trajectory remain insufficiently defined. This study mapped the bibliometric landscape, intellectual structure, and thematic evolution of this field. Publications were retrieved from the Web of Science Core Collection, PubMed, and Scopus on 5 June 2026, covering 2010-2026. After deduplication, screening, and eligibility assessment, 508 publications were included. Bibliometric and knowledge-mapping analyses were conducted using bibliometrix/Biblioshiny, VOSviewer, and CiteSpace.The final corpus comprised 508 publications from 328 publication sources, spanning 2011-2026, with an annual growth rate of 35.38%, 2102 contributing authors, 76,871 cited references, 4091 database-supplied index keywords, and 1844 author keywords. Publication activity accelerated markedly after 2022, with 438 records published during 2022-2026, accounting for 86.2% of the corpus. Exploratory life-cycle modeling was consistent with an early rapid-growth phase, although its estimates should be interpreted cautiously. The International Journal of Computer Assisted Radiology and Surgery was the most productive publication source, while China, the United States, and Italy led national scientific production. International co-authorship remained limited at 4.13%. Keyword and network analyses highlighted digital twin modeling, virtual and augmented reality, artificial intelligence, robotics, teleoperation, simulation, and human-robot interaction as prominent and increasingly interconnected themes.Overall, digital twin-enabled robotic surgery remains a rapidly expanding but formative research domain. Its focus is shifting from static virtual representation and simulation toward patient-specific modeling, surgical training, extended-reality interaction, teleoperation, scene understanding, and supervised robotic assistance. Future progress will require clearer definitions, interoperable data structures, real-time model updating, uncertainty-aware methods, multicenter validation, and clinically meaningful outcome evaluation.