Artificial Intelligence–Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education
- 1Department of Operating Room, Faculty of Allied Health Medicine, Iran University of Medical Sciences, Tehran, Iran
- 2Center for Educational Research in Medical Science (CERMS), Department of Medical Education, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- 3Department of Artificial Intelligence in Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
Abstract
Introduction:: Advancements in artificial intelligence (AI) and computer vision offer new opportunities to improve clinical skills training. This study developed a real-time, AI-based computer vision system for detecting surgical instruments and evaluated its association with clinical performance among surgical technology students.
Methods:: A quasi-experimental pre-test/post-test study with a control group was conducted among 48 eligible surgical technology students at Iran University of Medical Sciences during the 2025–2026 academic year. Participants who met the predefined inclusion criteria were enrolled using census sampling. Following ethics approval (IR.IUMS.REC.1403.928), the participants were allocated to an intervention group (n=31), which received immediate AI-generated visual feedback after each instrument arrangement task, and a control group (n=17), which received routine instructor-led training without AI feedback. A YOLOv8-based deep learning model was trained using transfer learning on annotated images of surgical table setups. Clinical performance was assessed using a validated observational checklist (CVI=0.90; Cronbach’s α=0.87). Data were analyzed using analysis of covariance (ANCOVA), the Wilcoxon signed-rank test, Spearman’s correlation, and the intraclass correlation coefficient (ICC) in SPSS version 27. All 48 participants completed the study (attrition rate=0%).
Results:: The AI model achieved excellent object detection performance (precision=0.974, recall=0.979, [email protected]=0.981, [email protected]:0.95=0.808), with an average inference time of 1.6 seconds per image. Clinical performance was significantly higher following AI-assisted feedback (Wilcoxon signed-rank test, Z=−4.879, p<0.001, r=0.88). After adjustment for baseline performance using ANCOVA, AI-assisted feedback was associated with higher post-intervention performance scores than conventional training (F (1,45)=19.51, p<0.001, partial η2=0.302). Excellent agreement was observed between AI-generated assessments and expert human ratings (ICC=0.984).
Conclusions:: Real-time, AI-based computer vision feedback was associated with improved surgical instrument arrangement performance among surgical technology students while providing an objective and reliable assessment of practical skills. The proposed system represents a promising proof-of-concept educational tool for simulation-based training and competency assessment and may complement conventional instructional approaches in surgical technology education.
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BibTeX
@article{kamboozia2026artificial,
title = {{Artificial Intelligence–Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education}},
author = {KIARASH KAMBOOZIA and SEDIGHEH HANNANI and NAZANIN SARRAF SHAHRI and ZEYNAB BARZEGAR and SAEID ZARIF GHASEMIAN},
journal = {Journal of Advances in Medical Education \& Professionalism},
year = {2026},
month = oct,
doi = {10.30476/jamp.2026.111362.2415},
url = {https://doi.org/10.30476/jamp.2026.111362.2415},
note = {CC BY-ND 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence–Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education
AU - KIARASH KAMBOOZIA
AU - SEDIGHEH HANNANI
AU - NAZANIN SARRAF SHAHRI
AU - ZEYNAB BARZEGAR
AU - SAEID ZARIF GHASEMIAN
PY - 2026
DA - 2026/10/01
JO - Journal of Advances in Medical Education & Professionalism
DO - 10.30476/jamp.2026.111362.2415
UR - https://doi.org/10.30476/jamp.2026.111362.2415
AB - Introduction:: Advancements in artificial intelligence (AI) and computer vision offer new opportunities to improve clinical skills training. This study developed a real-time, AI-based computer vision system for detecting surgical instruments and evaluated its association with clinical performance among surgical technology students. Methods:: A quasi-experimental pre-test/post-test study with a control group was conducted among 48 eligible surgical technology students at Iran University of Medical Sciences during the 2025–2026 academic year. Participants who met the predefined inclusion criteria were enrolled using census sampling. Following ethics approval (IR.IUMS.REC.1403.928), the participants were allocated to an intervention group (n=31), which received immediate AI-generated visual feedback after each instrument arrangement task, and a control group (n=17), which received routine instructor-led training without AI feedback. A YOLOv8-based deep learning model was trained using transfer learning on annotated images of surgical table setups. Clinical performance was assessed using a validated observational checklist (CVI=0.90; Cronbach’s α=0.87). Data were analyzed using analysis of covariance (ANCOVA), the Wilcoxon signed-rank test, Spearman’s correlation, and the intraclass correlation coefficient (ICC) in SPSS version 27. All 48 participants completed the study (attrition rate=0%). Results:: The AI model achieved excellent object detection performance (precision=0.974, recall=0.979, [email protected]=0.981, [email protected]:0.95=0.808), with an average inference time of 1.6 seconds per image. Clinical performance was significantly higher following AI-assisted feedback (Wilcoxon signed-rank test, Z=−4.879, p<0.001, r=0.88). After adjustment for baseline performance using ANCOVA, AI-assisted feedback was associated with higher post-intervention performance scores than conventional training (F (1,45)=19.51, p<0.001, partial η2=0.302). Excellent agreement was observed between AI-generated assessments and expert human ratings (ICC=0.984). Conclusions:: Real-time, AI-based computer vision feedback was associated with improved surgical instrument arrangement performance among surgical technology students while providing an objective and reliable assessment of practical skills. The proposed system represents a promising proof-of-concept educational tool for simulation-based training and competency assessment and may complement conventional instructional approaches in surgical technology education.
SN - 2322-2220
SN - 2322-3561
ER -