Ophthalmic surgery requires micrometer-level precision due to the eye’s delicate anatomy, yet manual limitations and restricted 3D visualization make absolute accuracy challenging, driving interest in robotic and Artificial Intelligence technologies to enhance safety and precision. This is a narrative review of experimental and published studies on PubMed and Open Evidence to review the current advances, challenges, and translational potential of robotic-assisted needle insertion in corneal surgery. Topics include robotic corneal surgery platforms such as the da Vinci and custom microsurgical robots, telemanipulation, intraoperative optical coherence tomography (iOCT), and reinforcement learning applications. Recent advancements in the field have demonstrated enhanced needle insertion precision, tremor elimination, and improved visualization of needle trajectory in corneal procedures, including corneal lacerations, pterygium repairs and penetrating keratoplasties (PKs). Nonetheless, significant limitations in the state of the art persist, particularly concerning the integration of robotic systems into clinical practice in in vivo settings. Our results indicate that current studies are mostly conducted in an ex vivo setting, which introduces inherent biases and reduces the generalizability of findings to clinical practice. Additionally, the majority of these studies involve small sample sizes, limiting statistical power and the ability to draw robust conclusions. Together, these limitations highlight the need for larger, well-designed in vivo studies to validate and expand upon existing findings. This review bridges experimental innovation and clinical application, highlighting strategies to overcome current barriers in robotic corneal surgery.
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Eliana-Ruobing Zhang
McGill University Health Centre
Andrés C. Ramos
McGill University
Giacomo Beschi
McGill University
Actuators
McGill University
McGill University Health Centre
University of Brescia
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Zhang et al. (Tue,) studied this question.
synapsesocial.com/papers/6940275a2d562116f28ffc1e — DOI: https://doi.org/10.3390/act14120587
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