Main Article Content
The growing use of electronic health history has caused in vast volumes of medicalinformation being collected. The number of data gathered in EHR systems in ophthalmology, forthe instance, has been steadily increasing. However, because of the data's complexity and heterogeneity, making actual secondary use of our current EHR information for enhancing individual care also aiding medical decision-making has proven difficult. Artificial intelligence approaches provide a viable method of analyzing multimodal informationgroups. Whereas AI approaches have been widely used to imaging data, only a few researchers have used AI techniques to analyze medicalstatistics from EHR. The goal of our current paper remains to offer an impression of several AI algorithms used in the area of ophthalmology to analyze EHR data. With application of AI approaches, secondary usage of EHR information has concentrated on glaucoma, diabetic retinopathy, age-connected macular degeneration, in addition spectacles, according to our current research. Such methods wereutilized to enhance the analysis, dangervaluation, also progression forecast of ocular diseases. The most popular tactics developed in the publications analyzed were supervised machine learning, deep learning, also language processing.