Clinical Report: AI-Enhanced Microsurgical Training Improves Technical Skills

Overview

This systematic review analyzed 13 studies evaluating artificial intelligence (AI) integration in microsurgical training, demonstrating improved technical performance, reduced errors, and enhanced learning efficiency compared to traditional methods. Despite promising results, evidence quality was very low with high risk of bias and limited external validation.

Background

Microsurgical training requires precise skill acquisition, traditionally relying on subjective assessment and expert feedback. Artificial intelligence offers objective, adaptive tools such as instrument tracking, motion analysis, and real-time coaching to enhance training outcomes. Various AI models including convolutional neural networks have been applied to improve technical skill assessment and guidance. However, the fragmented and heterogeneous nature of current evidence necessitates systematic evaluation to guide clinical adoption.

Data Highlights

Key Findings

• AI models such as Mask R-CNN, YOLOv2, and ResNet-50 were predominantly used for objective assessment and real-time guidance in microsurgical training.
• AI-enhanced training demonstrated improved technical skills, notably reducing surgical errors and enhancing learning curves.
• Real-time feedback from AI systems contributed to more efficient skill acquisition and promising skill retention outcomes.
• Most studies were single-centre with small sample sizes and heterogeneous designs, limiting generalizability.
• High risk of bias and very low certainty of evidence were noted, with poor external validation across studies.
• Reporting quality was generally high or moderate, but standardization of outcomes and methodologies was lacking.

Clinical Implications

AI integration in microsurgical training can provide objective, personalized feedback that enhances technical skill development and reduces errors in simulation settings. Clinicians and educators should interpret current evidence cautiously due to methodological limitations and advocate for standardized, multi-centre trials with robust validation before widespread clinical implementation. Ethical considerations and transparency in AI use remain essential for future adoption.

Conclusion

Artificial intelligence shows promising potential to augment microsurgical training by improving technical performance and learning efficiency. However, the current low-quality, heterogeneous evidence underscores the need for rigorous, standardized research to confirm clinical benefits and facilitate safe translation into practice.

References

1. Kiew et al. 2025 -- Artificial intelligence in microsurgery and supermicrosurgery training within plastic surgery: a systematic review
2. Kapila et al. 2024 -- Decoding the impact of AI on microsurgery: systematic review and classification of six subdomains for future development
3. Escobar-Castillejos et al. 2025 -- Transforming surgical training with AI techniques for training, assessment, and evaluation: scoping review