Artificial intelligence (AI) has been explored as a promising diagnostic aid for ocular surface diseases (OSDs). The spectrum of OSD ranges from highly prevalent benign conditions such as dry eye disease (DED) to rare but potentially dangerous disorders, including ocular surface squamous neoplasia (OSSN) and conjunctival melanoma. This review provides an overview of current applications of AI across the major categories of ocular surface pathology and specifically highlights anterior segment imaging modalities, including slit-lamp examination, optical coherence tomography (OCT), and in vivo confocal microscopy (IVCM). Meibography, tear film dynamics, biochemical profiling, and other DED-related measures are also examined. Across these domains, reported AI model performance matches or exceeds that of ophthalmologists, offering consistent, reproducible, and accurate approaches for guiding diagnosis. However, studies with limited external or prospective validation, variable labeling strategies, and small, device-specific datasets predominate in the current literature, thereby limiting generalizability. Large multicenter datasets, standardized diagnostic frameworks, multimodal integration, and prospective trials that assess human–AI cooperation in practical settings should be an emphasis in future research. By filling these gaps, AI systems could advance from experimental tools to clinically reliable applications that improve access and diagnostic accuracy in the care of ocular surface disease and tumors.

Ocular surface lesions represent a diverse group of pathologies which may be challenging to diagnose clinically. Anterior segment imaging—including anterior segment optical coherence tomography (AS-OCT), optical coherence tomography angiography (OCTA), ultrasound biomicroscopy (UBM), and in vivo confocal microscopy (IVCM)—provides valuable adjunct information for the diagnosis, management, and monitoring of these lesions. The present review aims to provide an update on the principles, current clinical applications, advantages, limitations, and recent advancements in the imaging modalities used for the evaluation of ocular surface lesions. Notable recent advancements include the application of artificial intelligence in the interpretation of AS-OCT, intraoperative use of AS-OCT, the development of three-dimensional UBM, and expanded applications of each modality for a variety of ocular surface lesions.