Artificial intelligence assisted simulation and surgical video analytics for ophthalmic surgery training and competence development

TL;DR

This paper explores how AI can support ophthalmic surgery training at different skill levels, from novices to experts, using simulation, video analytics, and clinical data.

Contribution

The paper introduces a structured framework for integrating AI into ophthalmic training aligned with the Dreyfus model of skill acquisition.

Findings

AI-enabled VR simulation helps novices develop muscle memory and standardized habits.

Computer-vision models assist advanced beginners in understanding surgical workflows and spatial cognition.

At the expert stage, AI analytics help benchmark techniques and identify blind spots for continuous improvement.

Abstract

Based on the Dreyfus model of skill acquisition, this article classifies the professional development of ophthalmologists into four stages: novice, advanced beginner, competent, and expert. In this review, artificial intelligence (AI) is operationally defined as data-driven algorithms that enable prediction, perception, and objective assessment from multimodal surgical data. We distinguish AI methods from immersive hardware, such as virtual reality (VR), which serves as a training interface that may or may not incorporate AI-driven assessment and feedback. Accordingly, this manuscript focuses on AI-enabled simulation, computer-vision-based surgical video understanding, and registry/EHR-driven clinical practice and training continuum. At the novice stage, AI-enabled assessment within VR simulation helps trainees form muscle memory and standardized operating habits. This is achieved through haptic-enabled modules and objective performance metrics. For advanced beginners, computer-vision models and attention-visualization techniques support surgical workflow understanding and structured debriefing, assisting trainees in building surgical logic and spatial cognition. When doctors reach the competent stage, AI uses large-scale clinical data to estimate complication risk and support scenario-based crisis training, strengthening complication management and non-technical skills. At the expert stage, AI-assisted surgical video analytics can benchmark technique patterns and surface potential blind spots, facilitating continuous calibration and knowledge sharing. Overall, the evidence to date suggests that AI is best positioned as an assistive tool to augment human learning and decision-making. However, generalizability, interpretability, data governance, and medicolegal accountability remain key barriers to safe and scalable deployment.

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