Background Real-time computer vision-based artificial intelligence (CV-AI) systems for surgical video analysis are rapidly advancing. Current evaluation strategies and clinical-readiness reporting, however, remain inconsistent. This review mapped contemporary CV-AI task domains, performance metrics, and indicators of clinical readiness for real-time intraoperative deployment within general surgery. Methods This study followed Joanna Briggs Institute methodology for scoping reviews, and was reported in accordance with PRISMA-ScR. Eligible studies were identified by systematic literature search of the MEDLINE, Embase, PubMed, and Scopus databases and published within the last ten years. Results Of 490 records screened, 113 studies met inclusion criteria after full-text review. Retrospective feasibility analyses predominated, with only 13 studies (12%) evaluating real-time intraoperative integration. Five task domains were identified (phase recognition, anatomy identification, action-event recognition, instrument-tracking, skill-assessment). Forty-one unique performance metrics were reported, with predominant use of discrimination-style summary measures (e.g. accuracy, recall, F1 score), and comparatively sparse reporting of class-imbalance, boundary-aware (e.g. Hausdorff distance) or real time workflow factors (e.g. latency/stability, interface design, surgeon feedback). External validation was described in 13 (12%) studies. Nine studies (8%) referenced AI-specific reporting frameworks. Conclusion Surgical CV-AI is advancing technically but remains predominantly early feasibility-stage. Variability in current metric application and limited real-time clinical evaluation limit potential for comparability, applicability and widespread adoption. Standardised metrics, evaluation frameworks, prospective clinical trials, and collaborative end-user engagement are critical to translate conceptual promise to reliable real-time decision-support tools that support surgeon judgement and integrate seamlessly into routine operative workflows.