Preview

Ophthalmology in Russia

Advanced search

The use of Artificial Intelligence in Ophthalmological Practice

https://doi.org/10.18008/1816-5095-2026-1-199-206

Abstract

The article discusses the current state and prospects for the use of artificial intelligence (AI) in ophthalmological practice. The main areas of AI application are described, including automated diagnostics of eye diseases, personalized treatment, prognosis of the course of diseases and support of surgical interventions. An analysis of the existing approaches and technologies, such as deep learning and computer vision, which are used to analyze medical images and data, is provided. Particular attention is paid to data standardization, safety and ethical aspects of introducing AI into clinical practice. The importance of cooperation between specialists in different fields for the effective implementation of innovative technologies and improving the quality of medical care is emphasized.

About the Authors

A. M. Chebenova
I.N. Ulianov Chuvash State University
Russian Federation

Chebenova Aigul M., student

Moskovsky ave., 15, Cheboksary, 428015



D. I. Yalakova
I.N. Ulianov Chuvash State University
Russian Federation

Yalakova Dilyara I., student

Moskovsky ave., 15, Cheboksary, 428015



N. V. Korsakova
I.N. Ulianov Chuvash State University
Russian Federation

Korsakova Nadezhda V., MD, Professor of the Ophthalmology and Otolaryngology Department 

Moskovsky ave., 15, Cheboksary, 428015



References

1. Kurysheva NI, Rodionova OYe, Pomerantsev AL, Sharova GA. Application of artificial intelligence in glaucoma. Part 2: neural networks and machine learning in monitoring and treatment of glaucoma. Annals of Ophthalmology. 2024;140(4):80– 85 (In Russ.). doi: 10.17116/oftalma202414004180

2. Lebedev DS, Zhuravleva NI. Artificial intelligence technologies in eye surgery. Russian Journal of Biomedical Engineering. 2021;20(10):789–795 (In Russ.).

3. Sheremetyev LD, Kostina EYu. Modern approaches to the use of artificial intelligence in glaucoma diagnosis. Health issues. 2020;18(3):128–134 (In Russ.).

4. Sidorov PV, Smirnov KK. Examples of using artificial intelligence in ophthalmology: experience of Russian clinics. Medical equipment. 2020;35(12):56–60 (In Russ.).

5. Kravtsov GN, Zakharova EP. AI in clinical decision support: prospects in ophthalmology. Innovative medicine. 2022;11:17–24 (In Russ.).

6. Kulikova TA, Chesnokov AL. The role of AI in management of chronic eye diseases. Practical medicine. 2020;25(16):67–72 (In Russ.).

7. Gerasimenko NE, Frolov KA. Machine learning in processing optical coherence tomography data. Neurocomputer development and application. 2021;21:43–49 (In Russ.).

8. Davydov VS, Martynyuk EA. Artificial intelligence in personalized therapy of eye diseases. Scientific papers of the Institute of Information Technology. 2022;22:58– 63 (In Russ.).

9. Nikitin YG, Pankov AF. Use of AI for early diagnosis of age-related macular degeneration. Current problems of ophthalmology. 2022;19(14):45–51 (In Russ.).

10. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2022;316(22):2402– 2410. doi:10.1001/jama.2016.17216.

11. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologistlevel classification of skin cancer with deep neural networks. Nature. 2023;542:115– 118. doi: 10.1038/nature21056.

12. Yim J, Lee S, Kim D, Park K. The role of artificial intelligence in the management of eye diseases: a review of the literature. Ophthalmic Res. 2020;63(1):1–9. doi: 10.1159/000502987.

13. Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Improved automated detection of diabetic retinopathy in a large population of patients with diabetes. JAMA Ophthalmol. 2021;134(5):530–536. doi: 10.1001/jamaophthalmol.2021.0530.

14. Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Schmetтерer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2021;103(2):167–175. doi: 10.1136/bjophthalmol-2018-313173.

15. Chen P, Zhang S, Xu J, Liu X, Wang Y. Artificial intelligence in ophthalmology: current applications and future directions. Curr Opin Ophthalmol. 2020;31(3):187– 192. doi: 10.1097/ICU.0000000000000668.

16. Lee CS, Lee AY, Baughman DM. Deep learning in ophthalmology: a review of the current state of the art and future directions. Ophthalmology. 2024;127(1):4–12. doi: 10.1016/j.ophtha.2023.07.002.

17. Mironov AV, Gromova OM. Practical application of artificial intelligence in retinal fundus image analysis. Journal of scientific publications of graduate students and doctoral students. 2021;15:32–37 (In Russ.).


Review

For citations:


Chebenova A.M., Yalakova D.I., Korsakova N.V. The use of Artificial Intelligence in Ophthalmological Practice. Ophthalmology in Russia. 2026;23(1):199-206. (In Russ.) https://doi.org/10.18008/1816-5095-2026-1-199-206

Views: 313

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1816-5095 (Print)
ISSN 2500-0845 (Online)