ReviewNPJ digital medicine2022
Machine learning for technical skill assessment in surgery: a systematic review.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 4 of them syntheses that pooled it.
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.
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
72 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Objective technical skills assessment in laparoscopic cholecystectomy: a systematic review of manual, kinematic, and AI-based tools.Surgical endoscopy · 2026Pooled it
- Identifying Research Priorities in Digital Education for Health Care: Umbrella Review and Modified Delphi Method Study.Journal of medical Internet research · 2025Pooled it
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- Video-based tools for surgical quality assessment of technical skills in laparoscopic procedures: a systematic review.Surgical endoscopy · 2023Pooled it
- Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study.Journal of endourology · 2024Trial
- Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.Surgical endoscopy · 2026Review
- Comparative learning curves of robotic and laparoscopic skills in a simulated task-based model.Journal of robotic surgery · 2026Observational
- 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
- Review
- Explainable machine learning for automated robotic surgical skill assessment using rich kinematic features.Journal of robotic surgery · 2026Article
- Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.NEJM AI · 2026Article
- Robotic-Assisted Thoracic Surgery in the Immunotherapy Era: Navigating Altered Anatomy, Oncologic Precision, and the Future of Integrated Platforms.Journal of clinical medicine · 2026Review
- An interpretable whole-stream video based deep learning framework for surgical skill assessment of basic procedural elements.International journal of surgery (London, England) · 2026Article
- A Conceptual Model for Embedding Automated Assessments in Graduate Medical Education.Journal of graduate medical education · 2026Article
- Dynamic analysis for employment substitution effect of robots fused with deep learning models and artificial intelligence.Scientific reports · 2026Article
- The future of robotic surgery in the age of artificial intelligence.Nature reviews. Urology · 2026Review
- An Australian perspective of using video for the assessment of laparoscopic surgery and support for artificial intelligence in performance evaluation.Langenbeck's archives of surgery · 2026Article
- Multimodal deep learning for objective skill assessment in robot-assisted vesico-urethral anastomosis.Journal of robotic surgery · 2026Article
- Article
- Artificial intelligence-enhanced microsurgical training: a systematic review.NPJ digital medicine · 2026Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive, and subject to bias. Machine learning (ML) has the potential to provide rapid, automated, and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66), and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed the performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment of basic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.PROSPERO: CRD42020226071.
Identifiers
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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.