Demystifying the Jargon: The Bridge between Ophthalmology and Artificial Intelligence

Abstract

Publications related to artificial intelligence (AI) and machine learning have risen exponentially in the past 5 years in the medical literature, including a number of articles involving retinal disease.^– The mathematical theories beneath machine learning methods have been around for decades but in most cases were too computationally intense to implement by hand.^, Recent advances in computer central processing units and graphics processing units have enabled the application of these models to solve real-world problems.^, This has led to rapid advances in the fields of AI and machine learning, specifically deep learning, and to a growing number of medical and ophthalmic applications.^–, As with the rapid evolution of any new technologies, there can be confusion about new terminologies, and AI is no exception. Though they are often used interchangeably, the terms “AI,” “machine learning,” “deep learning,” and “neural networks” are not synonymous and may be confusing for ophthalmologists to distinguish. Below, we attempt to define these terms in a manner accessible to both ophthalmologists and vision researchers (Figure 1).

Figure 1.

A Venn diagram of artificial intelligence approaches.