Author Login

Correspondence to Author: Niraj Kumar Yadav¹, Priyanshi Priya¹, Ahmad Husain², Deepti Joshi², Amol Singh Garcha¹. 

¹ Dr. KNS Memorial Institute of Medical Sciences, Barabanki, India ² Uttar Pradesh University of Medical Sciences, Saifai, India

DOI: 10.52338/joed.2025.5236

Abstract:

Artificial Intelligence (AI) has become an integral component of modern ophthalmology, with oculoplastic surgery representing a rapidly evolving subspecialty that stands to benefit from advances in automation and deep learning. Despite promising innovations, a comprehensive understanding of AI’s role in oculoplastic diagnosis and management remains limited. This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current landscape, clinical performance, and translational potential of AI applications in oculoplastic diseases published between 2000 and 2025. A structured search of PubMed, Scopus, and Embase identified 25 peer-reviewed studies involving AI-driven image analysis, disease classification, surgical planning, and prognostic modelling across eyelid, lacrimal, orbital, and periocular disorders. Studies were assessed for model performance, clinical utility, and methodological rigor. The including studies demonstrated that AI algorithms achieved diagnostic accuracies exceeding 90% in detecting periocular malignancies, outperforming or complementing traditional clinician-based assessment. Machine learning models also facilitated surgical planning and postoperative outcome prediction, contributing to enhance clinical workflow efficiency and reduced inter-observer variability. Nevertheless, limitations related to dataset heterogeneity, small sample sizes, and limited external validation constrain generalizability. AI holds significant promise in advancing precision and efficiency in oculoplastic care. Future research should prioritize multicentric validation, explainable AI frameworks, and integration with robotic-assisted surgery to enable safe and ethical clinical translation, ultimately bridging the gap between technological innovation and patient-centered ophthalmic practice.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Oculoplastic Surgery; Computer-Assisted Diagnosis; Periocular Neoplasms; Robotic Surgical Procedures; Explainable Artificial Intelligence.

Citation:

Dr. Niraj Kumar Yadav, Artificial Intelligence in Oculoplastic Surgery: A Systematic Review. Journal of Ophthalmology and Eye Disorders 2025.