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Artificial Intelligence in Surgical Feedback: A Systematic Scoping Review.

Patrick Barba¹, Stephanie Younan¹, Kimberley Luu¹

¹Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California.

Journal of Surgical Education

|March 1, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers a promising supplement for surgical education feedback. AI-driven feedback in simulations can be as effective as, or better than, human instruction, enhancing surgical training.

Keywords:

artificial intelligence feedback machine learning procedural skill simulation surgical education

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Area of Science:

Medical Education
Surgical Training
Artificial Intelligence

Background:

Traditional surgical education relies on subjective feedback.
Objective and scalable feedback methods are needed to enhance surgical training.
Artificial intelligence (AI) presents a potential solution for delivering consistent procedural feedback.

Purpose of the Study:

To systematically review AI applications for procedural feedback in surgical education.
To evaluate the current landscape and efficacy of AI-driven feedback systems.

Main Methods:

A systematic scoping review of literature from 2015-2025 was performed.
Studies detailing AI systems for surgical feedback were included following PRISMA guidelines.
Included studies underwent rigorous screening and risk of bias analysis.

Main Results:

18 studies met inclusion criteria, with 67% showing moderate risk of bias.
AI feedback was equivalent or superior to expert instruction in simulations.
Real-time AI feedback showed potential for faster error correction across various surgical specialties.

Conclusions:

AI is a significant supplement to traditional surgical feedback methods.
Further development in areas like otolaryngology requires interdisciplinary collaboration.
Addressing cost and user trust are key for widespread AI adoption in surgical education.