• Mohammad Dahrouj and John B Miller.
• Department of Ophthalmology, Retina Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
• Semin Ophthalmol. 2021 May 19; 36 (4): 341-345.

AbstractOphthalmology has been at the forefront of medical specialties adopting artificial intelligence. This is primarily due to the "image-centric" nature of the field. Thanks to the abundance of patients' OCT scans, analysis of OCT imaging has greatly benefited from artificial intelligence to expand patient screening and facilitate clinical decision-making.In this review, we define the concepts of artificial intelligence, machine learning, and deep learning and how different artificial intelligence algorithms have been applied in OCT image analysis for disease screening, diagnosis, management, and prognosis.Finally, we address some of the challenges and limitations that might affect the incorporation of artificial intelligence in ophthalmology. These limitations mainly revolve around the quality and accuracy of datasets used in the algorithms and their generalizability, false negatives, and the cultural challenges around the adoption of the technology.

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