Robotic-assisted beating heart surgery: a comprehensive review

Robotic-assisted minimally invasive cardiac surgery offers the potential to perform off-pump beating heart procedures without cardiopulmonary bypass, but active closed-loop algorithmic motion compensation, as distinct from image-stabilization, motion-tracking, and mechanical-stabilization approaches that have seen limited clinical use, has not yet entered human clinical trials despite two decades of research. This scoping review, conducted using the PRISMA-ScR framework, addresses three questions: what are the quantitative performance limits of current sensing, prediction, and control methods; what is the state of clinical translation; and what barriers prevent first-in-human deployment. Studies were included if they reported methods or quantitative results for sensing, predicting, or compensating cardiac motion in robotic surgery (61 studies included); non-robotic stabilization studies and non-English publications were excluded. Four databases were searched from 1998 to May 2026; formal risk of bias assessment was not performed, consistent with PRISMA-ScR guidance. Results were synthesized narratively by thematic area. Key results include: dual Fourier-series EKF achieves 0.73-0.81 mm RMSE at 180 ms horizon; neural network approaches reduce this by 60% at 160 ms latency; cascade MPC-AOB maintains <0.08 N RMS force error across threefold stiffness variation; and shared canonical control reduces surgeon tracking error by 41%. The primary barrier is a systems integration deficit rather than any isolated algorithmic shortcoming. Six open challenges are classified by difficulty. The evidence base is limited by predominant porcine model validation. This review was not pre-registered, and received no external funding.