Effectiveness and safety of robotic microsurgery in free-flap reconstruction: A systematic review and single-arm meta-analysis
Year: 2026
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: Robotic microsurgery has emerged as a promising advancement in plastic and reconstructive surgery, offering enhanced precision through tremor suppression and motion scaling during microvascular anastomosis. This systematic review and single-arm meta-analysis synthesizes contemporary clinical evidence on the safety, feasibility, and operative performance of robotic-assisted microvascular reconstruction. Literature searches were performed in PubMed, Embase, and Web of Science. Thirteen clinical studies involving 264 patients were included in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Robotic systems used were predominantly the SYMANI® Surgical System (Medical Micro Instruments S.p.A., Pisa, Italy), utilized in 212 patients (80%), followed by the da Vinci® Surgical System (Intuitive Surgical, Inc., Sunnyvale, California, USA), used in 54 patients (20%). Reconstructions most commonly involved the cranio-maxillofacial and head and neck regions. Across 338 robotic anastomoses, 67% were arterial and 33% venous, performed using either end-to-end (70%) or end-to-side (30%) techniques. Common free flaps included radial forearm, anterolateral thigh, free fibula, and ulnar forearm flaps. Pooled analysis demonstrated a high flap survival rate of 96% (95% CI 93-98%) and a mean anastomosis time of 39.1 min (95% CI 33.0-45.1). These findings indicate that robotic-assisted microsurgical anastomosis is clinically feasible, safe, and efficient, particularly in anatomically constrained operative fields. Further comparative and long-term studies are required to define its role relative to conventional microsurgical techniques.
Institutions: Queen Mary University of London, Fatmawati Hospital
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,470
Funder: Queen Mary University of London
OpenAlex ID: W7128956854
