Artificial intelligence is rapidly reshaping ocular oncology, particularly in differentiating benign choroidal nevi from malignant uveal melanomas. Advanced image-based algorithms are improving diagnostic precision, enabling earlier detection and timely referral critical factors in preserving vision and improving survival outcomes. AI-assisted pattern recognition is proving especially valuable in borderline or subtle lesions where clinical ambiguity often exists.
Beyond oncology, AI is enhancing broader ophthalmic practice. In glaucoma and diabetic retinopathy, machine learning driven retinal imaging platforms are supporting earlier risk stratification and progression monitoring. Emerging technologies such as adaptive optics imaging now allow near single-cell level visualization of retinal structures, opening new avenues for detecting microstructural changes before functional loss…
