LAUSR

A dvancements in technology, including the widespread adoption of electronic medical record keeping, expanding access to imaging techniques such as nonmydriatic fundus photography the expansion of telemedicine, and the application of artificial intelligence to the field of medicine, promise to revolutionize our ability to diagnose and treat neuro-ophthalmic disease. “Big data,” for our purposes referring to the dramatic increase in the amount of medical data accessible to researchers, has the potential to accelerate investigations into diagnosis and treatment of neuro-ophthalmic conditions. Meanwhile, machine learning may provide a feasible avenue for analyzing an ever-increasing volume of data. As neuro-ophthalmologists, harnessing big data may allow us to access large data sets as an avenue to study uncommon diseases. Thus, it is important for neuro-ophthalmologists to understand both the opportunities and the challenges of embracing big data. This companion article to “Predictive Value of International Classification of Diseases Codes for Idiopathic Intracranial Hypertension in a University Health System” by Khushzad et al (1) aims to discuss opportunities and challenges of using big data, specifically the use of claims-based data, such as International Classification of Diseases (ICD) codes, as a source of big data for neuro-ophthalmology research.