ReviewNan fang yi ke da xue xue bao = Journal of Southern Medical University2026
[Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].
Review in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
15 authors.
Funding
Abstract
Minimally invasive surgery has become an important approach for treating complex surgical conditions. As such surgical procedures are often associated with high risks and technical complexities, the surgeon's skills, efficiency of team collaboration, and capacities for emergency response all significantly affect the surgical outcomes. Establishing a robust surgical skill assessment and quality control system is therefore of vital importance to reduce the risks and enhance perioperative safety. In this context, the conventional methods for surgical skill assessment and quality control (e.g., subjective scoring, postoperative analysis, etc.) appear insufficient due to their inconsistent standards, time and labor intensity, and lack of real-time feedback. In recent years, artificial intelligence (AI), particularly computer vision-based deep learning technology, has demonstrated immense potential in surgical motion recognition, workflow phase analysis, and intraoperative safety monitoring, and has propelled the development of surgical skill assessment and quality control toward automation, objectivity, and precision. Herein the authors review the latest research progress in the application of AI in minimally invasive surgical skill assessment and quality control, focusing on AI-based skill assessment and quality control algorithm models. This review also addresses model generalization, privacy protection, multicenter applicability, and future clinical applications of intelligent precision surgery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.