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Related Experiment Video

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Artificial intelligence-enhanced microsurgical training: a systematic review.

Wameth Alaa Jamel1, Mohammed Jameel2, Ibrahim Riaz3

  • 1Department of Plastic and Reconstructive Surgery, Baghdad Al-Russafa Health Directorate, Baghdad, Iraq. wmd.alaa2015@gmail.com.

NPJ Digital Medicine
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances microsurgical training by providing objective feedback, improving technical skills and learning efficiency. However, current evidence is limited by low-quality studies, necessitating further research.

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Area of Science:

  • Medical Education
  • Surgical Training
  • Artificial Intelligence

Background:

  • Microsurgical training traditionally relies on subjective feedback and lengthy learning curves.
  • Artificial intelligence (AI) presents an opportunity for objective, adaptive skill enhancement in microsurgery.
  • Existing evidence on AI's effectiveness in microsurgical training is fragmented.

Purpose of the Study:

  • To systematically review and evaluate the efficacy of AI-enhanced microsurgical training compared to traditional methods.
  • To assess AI's impact on technical performance, learning efficiency, and skill retention.
  • To identify limitations and future research directions for AI in surgical education.

Main Methods:

  • Systematic review following PRISMA guidelines, searching major databases from January 2010.
  • Inclusion of 13 studies (2,056 records screened) with 3-50 participants.
  • Narrative synthesis of data on AI models (e.g., CNNs), outcomes, risk of bias, and evidence certainty (GRADE).

Main Results:

  • AI models primarily used for assessment or guidance, focusing on instrument tracking and motion analysis.
  • AI demonstrated improvements in technical skills (reduced errors) and learning curves through real-time feedback.
  • Median AI accuracy was 83.8%; promising skill retention noted, but evidence certainty was very low due to high risk of bias.

Conclusions:

  • AI offers objective metrics and personalized feedback, showing potential to enhance microsurgical training and technical performance in simulations.
  • Heterogeneous and low-quality evidence currently limits generalizability and clinical translation.
  • Future research requires multi-center RCTs, standardized outcomes, and ethical considerations.