Purpose of Review <p>This review presents an updated overview of the most recent innovations in the use of artificial intelligence (AI) for the interpretation of ocular fundus imaging and its clinical implications. We discuss foundational AI definitions, the technical aspects of training AI for analysis of ocular imaging and the spectrum of AI modalities currently being utilized. We present the reader with the most recent evidence for the utility of AI in large-scale screening protocols for ocular disorders, cardiovascular risk stratification and early detection of neurodegenerative and neuro-ophthalmic disorders.</p> Recent Findings <p>AI algorithms have been shown to reliably detect many neuro-ophthalmic disorders requiring urgent intervention, including papilledema and CRAO, on ocular imaging in absence of other relevant clinical information, often outperforming clinicians by detecting subtle structural abnormalities invisible to the human eye. Additionally, AI has revolutionized the search for non-invasive biomarkers of systemic disease with conception of the retinal age gap for early detection of neurodegenerative disorders, such as Alzheimer’s and Parkinson’s disease.</p> Summary <p>Currently, the use of AI for ocular imaging analysis primarily represents an important assistive diagnostic tool for the evaluation of ocular and neurological disorders. More validation studies must be performed to ensure that AI systems are generalizable to diverse patient populations. Further efforts to increase transparency on how AI algorithms make predictions will invariably promote earlier adoption of these systems in clinical settings. </p>