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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Related Experiment Video

Updated: May 5, 2026

A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
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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
PubMed
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 intelligencefeedbackmachine learningprocedural skillsimulationsurgical 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.