Applications of Artificial Intelligence in Neuro-Ophthalmology: Neuro-Ophthalmic Imaging Patterns and Implementation Challenges

Artificial intelligence (AI) can analyze imaging motifs, review large datasets, and integrate a wide array of clinical parameters. AI applications including machine learning and deep learning systems have been proposed to aid in the diagnosis of intracranial stroke, ischemic optic neuropathy, demyelinating diseases, and idiopathic intracranial hypertension. We review and update the recent literature on the potential role of AI in neuro-ophthalmology focusing on imaging. We discuss ongoing innovations in AI of relevance in neuro-ophthalmology (e.g. clinical decision support systems and prognosis predictions). There are also challenges in integrating AI into the practice of neuro-ophthalmology for the safety and efficacy of clinical medicine and potential ethical questions regarding AI enabled patient care. Given the manpower shortage of neuro-ophthalmology, however, the potential role of AI in neuro-ophthalmology may help to bridge the gap and unmet need for timely and appropriate neuro-ophthalmic care in the future.