SynthesisSurgical endoscopy2023
Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.
Synthesis in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Untangling surgical gesture analysis-are we even speaking the same language? a systematic review.Surgical endoscopy · 2025Pooled it
- Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.The Laryngoscope · 2026Article
- AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.Surgical endoscopy · 2026Article
- Analysis and mitigation of equipment-induced shortcuts in AI models for laparoscopic cholecystectomy.PLOS digital health · 2026Article
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Cross-Stream and Cross-Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Development and application of an intelligent assessment system for medical clinical skill training.NPJ digital medicine · 2026Article
- The future of robotic surgery in the age of artificial intelligence.Nature reviews. Urology · 2026Review
- A decade-long shift in use of energy devices for BABA robotic thyroidectomy: automated video analysis by deep learning.Journal of robotic surgery · 2026Article
- Utilising artificial intelligence to identify surgical anatomy during laparoscopic donor nephrectomy - a validation and feasibility study.Scientific reports · 2026Article
- Can artificial intelligence outperform experts in assessing clinical skills? Evidence from a comparative experiment.Frontiers in medicine · 2026Article
- The effects of human training data (HTD) explanation on purchase intention for artificial intelligence (AI) technologies.PloS one · 2026Article
- Prompt injection attacks on vision-language models for surgical decision support.npj digital surgery · 2026Article
- Surgical video-based temporal action analysis algorithm and competency assessment in laparoscopic cholecystectomy: development and exploratory evaluation.Surgical endoscopy · 2026Article
- Automated performance assessment in simulated laparoscopic crural repair.Surgical endoscopy · 2025Article
- Article
- A research roadmap for AI opportunities in student assessment for medical education.BMC medical education · 2025Review
- Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data.Journal of robotic surgery · 2025Article
- Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review.Journal of medical Internet research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTechnical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery.
methodsA systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.
resultsIn total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB.
conclusionAI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.
Indexed as
Identifiers
What OpenQuestion holds
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.