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Updated: Jan 20, 2026

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Automated and Artificial Intelligence (AI)-Derived Performance Assessment in Surgical Simulation: A Systematic

Ahmad Khalifa1, Owais Tahhan2, Mohammed Albazooni3

  • 1Surgery, University Hospitals Birmingham NHS Foundation Trust, Birmingham, GBR.

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|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in surgical education for performance assessment, but current methods lack consistent validity and reliability. Standardized, validated AI tools are needed for ethical and reproducible surgical simulation training.

Keywords:
artificial intelligenceautomated assessmentcompetency-based educationcomputer visionforce sensorsmotion trackingskill evaluationsurgical simulationsurgical trainingvirtual reality

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Area of Science:

  • Medical Education
  • Surgical Training
  • Artificial Intelligence

Background:

  • AI-assisted and automated performance assessment is growing in surgical education.
  • The extent, effectiveness, and trustworthiness of AI in surgical skill assessment remain unclear.

Purpose of the Study:

  • To systematically review the literature on AI-assisted and automated performance assessment in surgical education.
  • To evaluate the efficacy, validity, and reliability of AI algorithms in assessing technical surgical skills.

Main Methods:

  • Systematic review of studies from 2010-2025 in PubMed, Scopus, Embase, and IEEE Xplore.
  • Included experimental and observational studies on AI for technical skill assessment in simulation and clinical practice.
  • Extracted data on study characteristics, algorithms, performance measures, validity, reliability, and study quality.

Main Results:

  • Twenty-nine studies met inclusion criteria, predominantly using supervised learning algorithms.
  • Performance measures varied widely, with inconsistent documentation of validity and reliability.
  • A shift towards simulation-based assessment and multimodal data sources was observed, but methodological heterogeneity persists.

Conclusions:

  • AI-based assessment holds potential for surgical education, especially real-time adaptive feedback.
  • Methodological inconsistencies, lack of transparency, and unproven validity/reliability limit generalizability.
  • Standardized, validated methods are crucial for reproducible performance measures and ethical implementation in surgical simulation.