The treatment of arteritic anterior ischemic optic neuropathy (AAION), non-arteritic ischemic optic neuropathy (NAAION), and posterior ischemic optic neuropathy (PION) is a topic of ongoing research with mixed evidence on some pharmacotherapies and a need for more consensus. This manuscript provides an overview of these conditions’ current, potential future, and attempted pharmacotherapies. AAION’s current treatment regimen consists of high-dose steroids, with methotrexate, tocilizumab, and abatacept, being the most viable steroid-sparing therapy candidates. As for NAAION, the treatments being tried are vast, with mixed evidence supporting each modality. Similarly, despite the various treatment options explored, there still needs to be a universally effective therapy for PION. More research is needed to formulate an agreed-upon treatment regimen for these conditions.

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.