Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support.
Authors
Affiliations (8)
Affiliations (8)
- Department of Medicine and Surgery, University of Enna "Kore", Piazza dell'Università, 94100 Enna, Italy.
- Mediterranean Foundation "G.B. Morgagni", 95125 Catania, Italy.
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy.
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy.
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy.
- Department of Ophthalmology, Ospedale Sant'Antonio, Azienda Ospedaliera, 35127 Padova, Italy.
- Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd., London NW1 5QH, UK.
- Eye Clinic, Catania University, Policlinico G. Rodolico, 95121 Catania, Italy.
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.