A Survey of Versatile AI/Robotic Architectures for Ophthalmic Surgery Training

Abstract

Ocular surgery demands exceptional precision due to the eye’s delicate anatomy, where errors, particularly by novice surgeons, can lead to severe complications. This underscores the critical need for advanced training and skill development methodologies. The integration of versatile AI/Robotic architectures into ophthalmic surgical training is revolutionizing how surgeons acquire and refine their skills. These specialized training tools provide a safe and realistic environment, crucial for deliberate practice, skill enhancement, and the delivery of personalized feedback. This paper offers a comprehensive review of such AI/Robotic architectures specifically designed for or adapted to ophthalmic surgery training. It examines these systems from multiple viewpoints: for ophthalmologists, it details how these technologies are reshaping training paradigms, improving skill acquisition, and enabling competency-based educational models. For control and robotic engineers, it provides an in-depth technical analysis of contemporary training systems, with a focus on their control architectures, simulation environments, haptic feedback mechanisms, and varying levels of autonomy within these educational platforms. Furthermore, by identifying emerging commercial training simulators and AI-driven educational tools, this review highlights new market opportunities in the domain of surgical education. Ultimately, this comprehensive overview identifies promising directions for future research and development, offering valuable guidance for advancing the field of AI and robotics in ophthalmic surgical training.