ArticleBJS open2025
Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.
Article in BJS open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
The trial behind it
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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Who cites it
9 citing papers in PubMed.
- AI-Supported Prediction of Vesicoureteral Reflux in Children Based on Cystoscopic Configuration of the Ureteric Orifice.Journal of clinical medicine · 2026Article
- Surgical scene understanding and the structural validation gap in an industry-led AI ecosystem.NPJ digital medicine · 2026Article
- External validation and clinical readiness of intraoperative video AI in general surgery: a systematic review.Surgery today · 2026Review
- Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.NEJM AI · 2026Article
- Development and application of an intelligent assessment system for medical clinical skill training.NPJ digital medicine · 2026Article
- Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.Bioengineering (Basel, Switzerland) · 2026Article
- Beyond surgical showcase: workflow continuity and exploratory AI-oriented video interpretability of robotic low anterior resection videos on YouTube.Frontiers in surgery · 2026Article
- Evolving Paradigms in Minimal Access Surgery: A Comprehensive Review of Single-Incision Laparoscopic Appendicectomy.Cureus · 2026Review
- Decentralized, privacy-preserving surgical video analysis with Swarm Learning.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionSurgical videos are a valuable data source, offering detailed insights into surgical practice. However, video analysis requires specialist clinical knowledge and takes considerable time. Artificial intelligence (AI) has the potential to improve and streamline the interpretation of intraoperative video data. This systematic scoping review aimed to summarize the use of AI in the analysis of videos of surgical procedures and identify evidence gaps.
methodsSystematic searches of Ovid MEDLINE and Embase were performed using search terms 'artificial intelligence', 'video', and 'surgery'. Data extraction included reporting of general study characteristics; the overall objective of AI; descriptions of data sets, AI models, and training; methods of data annotation; and measures of accuracy. Data were summarized descriptively.
resultsIn all, 122 studies were included. More than half focused on gastrointestinal procedures (75 studies, 61.5%), predominantly cholecystectomy (47, 38.5%). The most common objectives were surgical phase recognition (40 studies, 32.8%), surgical instrument recognition (28, 23.0%), and enhanced intraoperative visualization (23, 18.9%). Of the studies, 79.5% (97) used a single data set and most (92, 75.4%) used supervised machine learning techniques. There was considerable variation across the studies in terms of the number of videos, centres, and contributing surgeons. Forty-seven studies (38.5%) did not report the number of annotators, and details about their experience were frequently omitted (102, 83.6%). Most studies used multiple outcome measures (67, 54.9%), most commonly overall or best accuracy of the AI model (67, 54.9%).
conclusionThis review found that many studies omitted essential methodological details of AI training, testing, data annotation, and validation processes, creating difficulties when interpreting and replicating these studies. Another key finding was the lack of large data sets from multiple centres and surgeons. Future research should focus on curating large, varied, open-access data sets from multiple centres, patients, and surgeons to facilitate accurate evaluation using real-world data.
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Registered trials
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