Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support

Abstract

Ocular surface malignancies include ocular surface squamous neoplasia (OSSN), con-junctival melanoma, and conjunctival lymphoma. These rare disorders may threaten vi-sion and, in some cases, life. Early identification may be difficult because malignant le-sions can resemble benign ocular surface conditions. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a potential aid to detection, classi-fication, and referral. This structured narrative review, organized with reference to SANRA principles, synthesizes PubMed/MEDLINE literature on AI applications in ocular surface oncology available through 29 August 2026. We examine slit-lamp photography, anterior segment optical coherence tomography, in vivo confocal microscopy, ultrasound biomicroscopy, and smartphone imaging. Deep-learning models have shown promising performance in selected binary OSSN tasks, whereas multiclass studies report lower sen-sitivity for rare malignancies such as conjunctival melanoma and lymphoma. Smartphone-based screening has also been evaluated in a prospective referral pathway, although the reference standard and verification process limit interpretation of popula-tion-level sensitivity. Dataset imbalance, image-acquisition variability, limited external and prospective validation, explainability, and workflow integration remain barriers to implementation. Future work should prioritize multicenter datasets, standardized acquisition, calibrated thresholds, and prospective human–AI evaluations. Current evidence supports further evaluation of AI as an adjunct for detection and referral, not as a re-placement for specialist assessment or histopathology.