Robot-Assisted Versus Conventional Mastectomy and Immediate Breast Reconstruction: A Systematic Review and Comparative and Single-Arm Meta-Analysis

Background Robot-assisted mastectomy (RAM) is increasingly adopted in breast and plastic surgery with proposed benefits including improved cosmetic outcomes, surgical precision, and surgeon ergonomics. However, concerns remain regarding longer operative times. This systematic review and meta-analysis aimed to evaluate outcomes of RAM with immediate breast reconstruction (IBR) of any type compared with conventional mastectomy (CM). Methods The study was conducted according to PRISMA guidelines. Database searches were conducted (May 1, 2025) to identify studies assessing RAM with IBR. Studies were included if they reported at least 25 RAM procedures and at least one predefined outcome. Two meta-analyses were performed: A) a comparative meta-analysis including both RAM and CM cohorts, and B) a single-arm meta-analysis with RAM studies without a control group. Primary effect measures included odds ratios (ORs) and mean difference (MD). Main findings Thirty studies comprising 3,985 patients were included. Margin status was comparable between RAM and CM (OR: 1.04, 95% CI: 0.43 to 2.52, p=0.93). RAM was associated with a significantly lower risk of overall complications (OR: 0.76, 95% CI: 0.63 to 0.92, p=0.004), reduced intraoperative blood loss, and longer hospital stay. A non-significant trend towards improved BREAST-Q scores and reduced risk of individual complications was observed with RAM. Surgery duration was significantly longer in the RAM group, with an estimated learning curve of 17 procedures required to achieve a significant reduction in operative time. Substantial heterogeneity was observed across studies (I 2 >50%). Conclusion Current evidence suggests that RAM with IBR is a feasible alternative to CM in selected patients, with comparable margin status and similar overall complication rates. However, the evidence is limited by heterogeneity, potential selection bias, and lack of long-term oncologic data.