SynthesisActa obstetricia et gynecologica Scandinavica2025
Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.
Synthesis in Acta obstetricia et gynecologica Scandinavica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- Automated surgical phase recognition in pediatric laparoscopic fundoplication using deep learning: a retrospective video-based study.Surgical endoscopy · 2026Article
- Deep learning assisting the surgical management of gynecologic cancers.Current opinion in oncology · 2026Review
- AI-powered semantic segmentation model for enhanced ureteral mapping and real-time instrument feedback in robotic colorectal surgery.Journal of robotic surgery · 2026Article
- Telesurgery and remote surgery research: a bibliometric analysis of scientific growth, collaboration, and thematic evolution.Journal of robotic surgery · 2026Article
- Global evolution of robot-assisted cholecystectomy research in the era of artificial intelligence: a bibliometric and knowledge-mapping study.Journal of robotic surgery · 2026Article
- Artificial intelligence and robotic surgery in emergency gastrointestinal procedures: a review of current evidence and future directions.Journal of robotic surgery · 2026Review
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Deep learning approach for critical exposure during division of the inferior mesenteric artery in colorectal surgery.Journal of robotic surgery · 2026Article
- Comparative feasibility of reasoning and non-reasoning large language models for gynecologic cancer emergency care.BMC health services research · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis systematic review examines the application of multiple deep learning algorithms in the analysis of intraoperative videos to enable feature extraction and pattern recognition of surgical phases, anatomical structures, and surgical instruments. MATERIAL AND
methodsA comprehensive literature search was conducted across PubMed, Web of Science, and EBSCO, covering studies published until March 2024. This review includes studies that applied AI models in the operating room for surgical-phase recognition and/or anatomical structures and instruments. Only studies utilizing machine learning or deep learning for surgical video analysis were considered. The primary outcome measures were accuracy, precision, recall, and F1 score.
resultsA total of 21 studies were included. Multilayer architecture of interconnected neural networks was predominantly used. The deep learning models demonstrated promising results, with accuracy ranging from 81% to 93.2% for surgical-phase recognition. Anatomical structure recognition models achieved accuracy between 71.4% and 98.1%.
conclusionsArtificial intelligence has the potential to significantly improve surgical precision and workflow, with demonstrated success in phase recognition and anatomical structure identification. However, further research is needed to address dataset limitations, standardize annotation protocols, and minimize biases.
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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.