Enhancing Robotic Surgery with AI and Imaging Navigation: Bridging Precision and Efficiency

Robotic surgery faces persistent challenges in real-time anatomical navigation during complex procedures like partial nephrectomy (PN), where millimeter-scale precision impacts oncological and functional outcomes. This review explores the integration of artificial intelligence (AI) and augmented reality (AR)-based holographic imaging to overcome these limitations, synergistically advancing surgical precision and operational efficiency.

Holographic imaging, an AR technique reconstructed from CT/MRI via surface rendering, provides detailed 3D anatomical models for preoperative planning, patient counseling, surgical training, and intraoperative navigation. These models enable precise tumor localization, super-selective vascular clamping, and parenchymal preservation, in particularly the PN.

Clinical outcomes demonstrate significant improvements. AI-automated modeling cuts 3D model reconstruction time while improving segmentation accuracy. AR navigation overlays virtual models onto endoscopic views, reducing collecting system injury and increasing enucleation rates for endophytic tumors. AI-based holographic imaging visualization alters surgical strategy for complex cases, reducing conversion from partial to radical nephrectomy.

Challenges persist in tracking robustness due to intraoperative organ deformation, but emerging innovations like fluorescence-guided AI registration and multi-sensor fusion—combining infrared markers and deformable algorithms—promise enhanced accuracy. Future directions include cloud-based AI platforms for scalable, real-time model generation; adaptive navigation systems trained on multi-institutional datasets to predict anatomical shifts; and cost-reduction strategies to broaden clinical adoption.