Artificial intelligence-augmented robotic surgery in gynecologic oncology: intraoperative assistance and analytics

Purpose of review Artificial intelligence (AI) is increasingly integrated into robotic-assisted surgery, transforming digital surgical platforms into data-driven environments capable of supporting intraoperative decision-making. In gynecologic oncology, where procedures require high precision and follow relatively standardized workflows, AI technologies may improve surgical quality, reduce variability, and enable safer surgical treatment strategies. This review summarizes recent advances in AI-augmented robotic-assisted surgery, focusing on intraoperative guidance, surgical analytics, and predictive decision support. Recent findings Recent literature demonstrates rapid progress in computer vision models capable of recognizing surgical phases, anatomical landmarks, and procedural steps in real time. Integration of augmented reality, fluorescence imaging, and multimodal imaging platforms is enhancing intraoperative visualization and anatomical orientation. Surgical data science approaches enable objective assessment of technical performance through video-based metrics and machine learning algorithms. Predictive analytics models integrating clinical and imaging data show promising results for risk stratification and outcome prediction, although most applications remain in early stages of validation. Summary AI-augmented robotic surgery has the potential to improve precision, standardization, and decision-making in gynecologic oncology. However, clinical implementation requires prospective validation, standardized datasets, and integration into routine workflows. Future developments will likely focus on multimodal data integration and context-aware decision support systems.