Affiliation:
1. Rostov State Medical University
2. Bashkir State Medical University
3. Academician Pavlov First Saint Petersburg State Medical University
Abstract
Currently, artificial intelligence is actively being introduced into various spheres of life, and medicine is no exception. In ophthalmology, the use of artificial intelligence is very promising, given that the diagnosis and therapeutic monitoring of eye diseases often depend heavily on the correct interpretation of images. The use of artificial intelligence in ophthalmology focuses on eye diseases that lead to vision loss, such as age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Over the past few years, artificial intelligence has reached tremendous successes in the practice of ophthalmology. Many studies have shown that artificial intelligence performance is equal to and even exceeds the capabilities of ophthalmologists in many diagnostic and prognostic tasks. However, there is still a lot of work to be done before introducing artificial intelligence into routine clinical practice. Issues such as real-world performance, generalizability, and interpretability of artificial intelligence systems are still poorly understood and will require more attention in future research. Most artificial intelligence-based systems are used in developed countries, and some require further study. High costs and a shortage in doctors and equipment in some regions of the Russian Federation and rural areas make it difficult to screen for eye diseases. Although the field of artificial intelligence is underdeveloped, we hope that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.
Reference47 articles.
1. Khusanov UA, Kudratillaev MB, Siddikov BN, Dovletova SB. Artificial intelligence in medicine. Science and Education. 2023;4(5): 772–782.
2. Gaifullin EO. Artificial intelligence in medicine. Ceteris paribus. 2023;(5):118–122. EDN: GQXLIT
3. Deep Learning and Glaucoma Specialists
4. Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images
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