Abstract: The integration of artificial intelligence (AI) into ophthalmic subspecialties, encompassing oculoplastics, is rapidly evolving. AI has demonstrated significant promise in enhancing diagnostic accuracy, streamlining clinical workflows, and personalizing surgical decisionmaking. This review aims to summarize current applications of AI in oculoplastics, identify major challengeslimitingits broader adoption, and explore future directions for research and clinical translation. We provide a narrative review of peer-reviewed studies and recent developments in AI-assisted diagnosis, image analysis, surgical planning, and patient monitoring within the domain of oculoplastics. Emerging AI applications include automated detection of eyelid tumors, facial analysis for ptosis and orbital disorders, preoperative planning, and postoperative outcome assessment. However, challenges such as limited data diversity, lack of interpretability, regulatory barriers, and ethical considerations persist. AI holds great promise in augmenting oculoplastic care. Moving forward, multidisciplinary collaboration, clinical validation, and advances in multimodal learning will be key to realizing the full potential of AI in this feld.
Hui et al. (Thu,) studied this question.