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Artificial intelligence in microsurgery and supermicrosurgery training within plastic surgery: A systematic review
Cheuk Ying Kyleen Kiew1, Anuska Shah1, Michalis Hadjiandreou2
1Faculty of Medicine, Imperial College London, Ayrton Road, South Kensington, London, United Kingdom.
JPRAS Open
|November 11, 2025
Summary
Artificial Intelligence (AI) enhances plastic surgery training by objectively assessing microsurgical skills. AI models accurately track hand and eye movements, improving trainee proficiency and surgical outcomes.
Area of Science:
- Plastic Surgery
- Microsurgery Training
- Artificial Intelligence
Background:
- Artificial Intelligence (AI) integration in plastic surgery is growing.
- Microsurgery and supermicrosurgery present steep learning curves.
- Existing simulation models offer inadequate preparation and fidelity.
Purpose of the Study:
- To systematically review and evaluate AI applications in microsurgery and supermicrosurgery plastic surgery training.
- To assess the future potential of AI in enhancing surgical skill acquisition.
Main Methods:
- Systematic literature search of PubMed, Embase, Scopus, and Google Scholar following PRISMA guidelines.
- Inclusion of studies on AI applications in microsurgery and supermicrosurgery training.
- Data extraction and risk of bias assessment using ROBINS-I tool.
Main Results:
- Five articles were included, focusing on AI applications in surgical skill assessment.
- AI models achieved >95% accuracy in classifying microsurgical skills using force-based models.
- Eye and instrument tracking parameters (e.g., blink rate, gaze, path length) were accurately analyzed by AI.
Conclusions:
- This is the first systematic review on AI in microsurgery and supermicrosurgery training.
- AI models demonstrate accuracy and precision in assessing microsurgical skills through motion tracking.
- AI offers scalable, objective methods for real-time monitoring, improving training and patient outcomes.
