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Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review
David Escobar-Castillejos1, Ari Y Barrera-Animas1, Julieta Noguez2
1Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico.
Journal of Medical Internet Research
|November 18, 2025
Summary
Artificial intelligence (AI) enhances surgical training by offering personalized feedback and adaptive learning paths. This review highlights AI
Area of Science:
- Surgical Education
- Medical Technology
- Artificial Intelligence
Background:
- Artificial intelligence (AI) presents new opportunities for surgical training assessment and evaluation.
- AI has the potential to improve upon traditional educational methods in surgery.
Purpose of the Study:
- To conduct a scoping review on the integration of AI in surgical training, assessment, and evaluation.
- To determine how AI can enhance surgical trainees' learning paths and performance using data-driven insights and predictive analytics.
- To identify current AI applications and future research directions in surgical education.
Main Methods:
- Searched PubMed, Scopus, and Web of Science for studies from January 2020 to March 2024, adhering to PRISMA-ScR guidelines.
- Included English-language full-text articles on AI in surgical training, assessment, or evaluation; excluded non-English texts, reviews, and preprints.
- Analyzed 56 selected studies, categorizing them by surgical procedure, AI technique, and training setup, with narrative synthesis and frequency tables.
Main Results:
- AI is frequently applied in minimally invasive surgery, neurosurgery, and laparoscopy, utilizing techniques like machine learning and deep learning.
- Simulation platforms and box trainers are common training setups, with AI providing automated skill assessment and personalized feedback.
- Studies reported improvements in trainees' learning curves and technical proficiency, though heterogeneity and lack of algorithmic transparency were noted.
Conclusions:
- AI in surgical training shows potential for enhancing skill acquisition and creating personalized, adaptive learning pathways.
- Limitations include small sample sizes, lack of standardized metrics, and insufficient external validation of AI models.
- Future research should focus on clarifying AI methodologies, improving reproducibility, and developing scalable, simulation-based solutions.
