Artificial Intelligence for Standardisation and Quality Assessment in Robotic Colorectal Surgery: A Narrative Review

Abstract

BACKGROUND: Robotic colorectal surgery has achieved widespread clinical adoption, yet meaningful standardisation of intraoperative practice remains limited, with persistent variability in operative setup, workflow execution, and technical performance across surgeons and institutions. METHODS: A narrative review was conducted using PubMed and Medline to identify studies addressing artificial intelligence applications in robotic colorectal surgery and other robotic specialities, focusing on workflow analysis, technical performance assessment, and intraoperative assistance. RESULTS: Available evidence indicates that artificial intelligence-based analysis of surgical video and robotic system data can characterise workflow patterns, technical execution, and safety relevant events, supporting process-based quality assessment. Nevertheless, important limitations persist, including restricted generalisability, limited interpretability of some models, and insufficient validation against clinically meaningful endpoints. CONCLUSIONS: Artificial intelligence is unlikely to standardise robotic colorectal surgery through automation. Its principal value lies in observation and measurement of the surgical process, enabling mediated standardisation while preserving surgeon judgement and professional responsibility.

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