AI-driven robotic surgery in oncology: advancing precision, personalization, and patient outcomes

Abstract

Artificial intelligence (AI) integrated with robotic systems is transforming oncologic surgery by significantly improving precision, safety, and personalization. The review critically explores the current landscape of AI-powered robotic technologies in tumor resection across various specialties, including urology, neurosurgery, orthopedics, pediatrics, and head and neck oncology. Despite rapid advancements, challenges remain in tumor boundary detection, real-time intraoperative navigation, motion compensation, and seamless data integration. Drawing on evidence from 22 recent clinical studies, pilot trials, and simulation-based research, the review identifies key innovations such as image-free robotic palpation, sensor-assisted feedback, 3D anatomical modeling, and adaptive motion management in radiotherapy. These technologies contribute to enhanced surgical accuracy, reduced invasiveness, and improved intraoperative decision-making. However, barriers such as inconsistent clinical protocols, limited cost-effectiveness data, and variability in performance across tumor types continue to hinder widespread adoption. Challenges persist in complex fields such as pediatric and neurosurgical oncology, where anatomical variability and safety concerns demand more advanced solutions. The review emphasizes the need for interoperable AI-robotic platforms, robust real-time analytics, and standardized safety frameworks. It also highlights the importance of ethical governance and clinician training in ensuring responsible implementation. In conclusion, AI-powered robotic surgery represents a major shift in oncology, offering the potential to improve long-term outcomes and reduce recurrence through data-driven, minimally invasive interventions. Realizing the potential will require interdisciplinary collaboration, longitudinal clinical validation, and strategic integration into healthcare systems.

Keywords: Adaptive robotic motion control; Artificial intelligence in surgical oncology; Minimally invasive oncologic robotics; Real-time intraoperative imaging; Robotic-assisted tumor resection.