Abstract
Background
Microsurgery is associated with a steep learning curve that requires extensive training
through supervised surgeries, cadaver practice, and simulations. The emergence of
artificial intelligence (AI) in medical education offers a new potential avenue for
microsurgery training by providing real-time feedback, performance analytics, and
advanced simulation. This study aims to evaluate the scope, implementation, and outcomes
of AI in microsurgical education for trainees across all levels.
Methods
A systematic review was performed in October 2024 following the Preferred Reporting
Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews
(PRISMA-ScR) guidelines. Four databases, including Embase, PubMed, Scopus, and Web
of Science, returned 3,323 citations. Inclusion criteria were studies investigating
the use of AI in the medical education of microsurgical trainees. Abstracts, commentaries,
editorials, systematic reviews, and non-English studies were excluded. After two-stage
screening, a total of 16 studies were included in this review.
Results
The assessed AI interventions appeared in the following number of studies: Computer
Vision (n = 13), Sensor-Driven Models (n = 2), Classical/Statistical Machine Learning (n = 4), Task-Specific Neural Networks (n = 4), Transfer Learning of Neural Networks (n = 3), Zero-Shot Inference of Pretrained Models (n = 5), Augmented/Virtual Reality (n = 5), and Anatomical Landmark Tracking (n = 5). Upon full data extraction, three overarching themes were identified among studies:
(1) Objective Assessment of Microsurgical Skills, (2) Innovations in Microsurgical
Education Materials, and (3) Improvement of Surgeon Workload and Performance. AI improved
skill assessment (accuracy: 0.74–0.99), training, and workload optimization. AI-enhanced
microsurgical training reduced training time (p = 0.015), improved ergonomics, and minimized cognitive load, accelerating learning
(β = 0.86 vs. β = 0.25).
Conclusion
AI has transformative potential in microsurgical education and practice, as emphasized
by its capacity to enhance skill assessment, educational tools, and ergonomic support.
Despite these enhancements, additional work is needed to address challenges such as
data bias, standardization, and real-world implementation.
Keywords
artificial intelligence - microsurgery - education
