Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review
Journal
Journal of Medical Internet Research
ISSN
1438-8871
Publisher
JMIR Publications Inc.
Date Issued
2025-11-18
Author(s)
Julieta Noguez
Alejandra J Magana
Bedrich Benes
Type
text::journal::journal article
Abstract
<jats:sec>
<jats:title>Background</jats:title>
<jats:p>Artificial intelligence (AI) has introduced novel opportunities for assessment and evaluation in surgical training, offering potential improvements that could surpass traditional educational methods.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Objective</jats:title>
<jats:p>This scoping review examines the integration of AI in surgical training, assessment, and evaluation, aiming to determine how AI technologies can enhance trainees’ learning paths and performance by incorporating data-driven insights and predictive analytics. In addition, this review examines the current state and applications of AI algorithms in this field, identifying potential areas for future research.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, the PubMed, Scopus, and Web of Science were searched for studies published between January 2020 and March 18, 2024. Eligibility criteria included English-language full-text articles that investigated the application of AI in surgical training, assessment, or evaluation; non-English texts, reviews, preprints, and studies not addressing AI in surgical education were excluded. After duplicate removal and screening, 56 studies were included in the analysis. Data were structured by categorizing studies according to surgical procedure, AI technique, and training setup. Results were synthesized narratively and summarized in frequency tables.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>From 1400 initial records, 56 studies met the inclusion criteria. Most were journal articles (84%, 47/56), with the remainder being conference papers (16%, 9/56). AI was most frequently applied in minimally invasive surgery (27%, 15/56), neurosurgery (20%, 11/56), and laparoscopy (16%, 9/56). Common techniques included machine learning (20%, 11/56), clustering (14%, 8/56), deep learning (11%, 6/56), convolutional neural networks (11%, 6/56), and support vector machines (11%, 6/56). Training setups were dominated by simulation platforms (33%, 19/56) and box trainers (24%, 13/56), followed by surgical video analysis (16%, 9/56), and robotic systems such as the da Vinci platform (13%, 7/56). Across studies, AI-enhanced training environments provided automated skill assessment, personalized feedback, and adaptive learning trajectories, with several reporting improvements in trainees’ learning curves and technical proficiency. However, heterogeneity in study design and outcome measures limited comparability, and algorithmic transparency was often lacking.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>The application of AI in surgical training demonstrates the potential to enhance skill acquisition and support more efficient, personalized, and adaptive learning pathways. Despite encouraging findings, several limitations exist, including small sample sizes, the lack of standardized evaluation metrics, and insufficient external validation of AI models. Future studies should aim to clarify AI methodologies, improve reproducibility, and develop scalable, simulation-based solutions aligned with global education goals.</jats:p>
</jats:sec>
<jats:title>Background</jats:title>
<jats:p>Artificial intelligence (AI) has introduced novel opportunities for assessment and evaluation in surgical training, offering potential improvements that could surpass traditional educational methods.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Objective</jats:title>
<jats:p>This scoping review examines the integration of AI in surgical training, assessment, and evaluation, aiming to determine how AI technologies can enhance trainees’ learning paths and performance by incorporating data-driven insights and predictive analytics. In addition, this review examines the current state and applications of AI algorithms in this field, identifying potential areas for future research.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, the PubMed, Scopus, and Web of Science were searched for studies published between January 2020 and March 18, 2024. Eligibility criteria included English-language full-text articles that investigated the application of AI in surgical training, assessment, or evaluation; non-English texts, reviews, preprints, and studies not addressing AI in surgical education were excluded. After duplicate removal and screening, 56 studies were included in the analysis. Data were structured by categorizing studies according to surgical procedure, AI technique, and training setup. Results were synthesized narratively and summarized in frequency tables.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>From 1400 initial records, 56 studies met the inclusion criteria. Most were journal articles (84%, 47/56), with the remainder being conference papers (16%, 9/56). AI was most frequently applied in minimally invasive surgery (27%, 15/56), neurosurgery (20%, 11/56), and laparoscopy (16%, 9/56). Common techniques included machine learning (20%, 11/56), clustering (14%, 8/56), deep learning (11%, 6/56), convolutional neural networks (11%, 6/56), and support vector machines (11%, 6/56). Training setups were dominated by simulation platforms (33%, 19/56) and box trainers (24%, 13/56), followed by surgical video analysis (16%, 9/56), and robotic systems such as the da Vinci platform (13%, 7/56). Across studies, AI-enhanced training environments provided automated skill assessment, personalized feedback, and adaptive learning trajectories, with several reporting improvements in trainees’ learning curves and technical proficiency. However, heterogeneity in study design and outcome measures limited comparability, and algorithmic transparency was often lacking.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>The application of AI in surgical training demonstrates the potential to enhance skill acquisition and support more efficient, personalized, and adaptive learning pathways. Despite encouraging findings, several limitations exist, including small sample sizes, the lack of standardized evaluation metrics, and insufficient external validation of AI models. Future studies should aim to clarify AI methodologies, improve reproducibility, and develop scalable, simulation-based solutions aligned with global education goals.</jats:p>
</jats:sec>