Artificial intelligence for intraoperative surgical guidance in robotic-assisted ventral cavity surgery: a systematic review on the current state of validation methods

Robotic-assisted surgery (RAS) has improved dexterity, three-dimensional (3D) visualization, and precision. Artificial intelligence (AI) is increasingly being explored to further enhance intraoperative guidance, and the reliable translation of these methods into clinical practice requires both rigorous technical validation and, ultimately, clinical validation demonstrating patient benefit. A systematic review was conducted by searching PubMed and Scopus for publications on computer vision methods for intraoperative guidance in abdominal, thoracic, and pelvic robotic surgery, yielding 2,601 results, of which 95 were included after screening. This review characterizes current technical validation practices and examines the extent to which clinical validation is performed. Key shortcomings were identified, including limited clinical validation, inconsistent methodologies, poor metric transparency, and the use of evaluation metrics with unclear clinical relevance, collectively hindering interpretation, comparison, and clinical implementation. Future studies should adopt clinically relevant, standardized metrics, ensure transparent reporting, and integrate real-world validation to improve reproducibility and facilitate the safe clinical implementation of AI in robotic surgery.