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Artificial intelligence applications in surgical education and training: a systematic review
Talia Tene1, Paulina Elizabeth Valverde Aguirre2, Ángel Floresmilo Parreño Urquizo3
1Department of Chemistry, Universidad Técnica Particular de Loja, Loja, Ecuador.
Frontiers in Artificial Intelligence
|July 9, 2026
Summary
Artificial intelligence (AI) in surgical education offers objective feedback for task-specific gains. However, current studies need standardized metrics for broader application in training.
Area of Science:
- Medical Education
- Artificial Intelligence
- Surgical Training
Background:
- Surgical training evolves with increasing complexity and patient safety demands.
- Artificial intelligence (AI) offers potential for data-driven, scalable surgical education.
- AI can provide objective feedback and cognitive support to trainees.
Purpose of the Study:
- To systematically review the literature on AI applications in surgical education.
- To assess the impact and outcomes of AI-driven surgical training methods.
- To identify trends and limitations in current AI-assisted surgical training research.
Main Methods:
- A PRISMA-guided systematic review was performed.
- Searches included PubMed, Scopus, and IEEE Xplore for studies from 2020-2025.
- PICO framework guided the study selection and data extraction.
Main Results:
- Twenty-one studies met inclusion criteria, covering diverse AI tools (deep learning, computer vision, predictive models).
- Training modalities included simulation-based, robotic, laparoscopic, and computer-assisted approaches.
- Positive effects were reported in 76.2% of studies, primarily on performance outcomes (81.0%).
Conclusions:
- AI shows promise for objective assessment and adaptive instruction in surgery.
- Task-specific performance improvements are noted with AI feedback.
- Further multicenter longitudinal studies with standardized metrics are essential to validate AI's role.