SUMMARY
Image recognition artificial intelligence represents a new milestone in modern medical technology since it has become a helpful tool in identifying suspicious changes and diseases, patient monitoring, and predicting treatment outcomes. Especially through the implementation of convolutional neural networks in the modelling of computer-based artificial intelligence systems, its clinical applicability has recently increased dramatically. Ophthalmology, particularly retinology, where diagnosis almost entirely relies on imaging, is highly technology-driven and as such uniquely positioned to bring AI innovations into clinical use. However, the integration of AI in the screening, diagnosis and treatment of ophthalmic diseases is however still limited, mainly due to the over-generalisation of lesion detection and the poor ability to identify different clinical entities simultaneously. This articleis focused on recent artificial intelligence algorithms and software, which are primarily aimed to support the detection of the most common retinal diseases and have been, or will be - with improved specificity and sensibility, introduced into clinical practice.