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The rapid evolution of artificial intelligence (AI) has catalyzed significant advancements in ophthalmology. As refractive surgery necessitates increasing levels of precision and personalization, AI offers pivotal solutions for optimizing clinical outcomes. This review systematically summarizes recent progress in applying machine learning and deep learning models to corneal refractive surgery and implantable collamer lens (ICL) procedures. Specifically, we examine AI's utility in preoperative candidate screening, personalized surgical planning, and the prediction of postoperative complications. Although AI demonstrates broad prospects for enhancing surgical decision-making, several challenges remain, including data standardization, algorithm interpretability, cross-device compatibility, and ethical considerations. Overall, AI-driven decision-support systems are accelerating the transition of refractive surgery from standardized protocols to data-driven, individualized management, with the potential to enable more intelligent, automated, and precise surgical correction in ophthalmology.
Ji et al. (Thu,) studied this question.