THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF RETINAL DISEASES

Krystsina Babkova; Vladimir Gurskii; Maryia Bohdzel; Yuliya Hlukhava; Iuliia Ilina; Alicja Korpacka; Patrycja Anna Borowiecka; Pavel Proborshch; Nadzeya Skadorva; Raman Sazon

doi:10.31435/ijitss.2(50).2026.5560

Authors

Krystsina Babkova National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0006-9580-4520
Vladimir Gurskii University Clinical Hospital in Poznań, Przybyszewskiego st. 49, Poznan, Poland https://orcid.org/0009-0001-3630-3738
Maryia Bohdzel Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0006-0920-2225
Yuliya Hlukhava National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland https://orcid.org/0009-0002-7887-6937
Iuliia Ilina Hospital of Our Lady of Perpetual Help in Wołomin https://orcid.org/0009-0008-3240-1358
Alicja Korpacka Our Lady of Perpetual Help Hospital, ul. Gdyńska 1/3, 05-200 Wołomin, Poland https://orcid.org/0009-0002-3638-5312
Patrycja Anna Borowiecka Our Lady of Perpetual Help Hospital, ul. Gdyńska 1/3, 05-200 Wołomin, Poland https://orcid.org/0009-0009-4861-3053
Pavel Proborshch Our Lady of Perpetual Help Hospital, Wołomin, Poland https://orcid.org/0009-0005-7377-1695
Nadzeya Skadorva Jan-Mikulicz Radziecki University Clinical Hospital in Wrocław, Borowska 213, Wrocław, Poland https://orcid.org/0009-0008-1267-1641
Raman Sazon Provincial Hospital in Bielsko-Biała, Bielsko-Biała, Poland https://orcid.org/0009-0001-6460-8758

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.5560

Keywords:

Artificial Intelligence, Deep Learning, Retinal Diseases, Diabetic Retinopathy, Age-Related Macular Degeneration, Glaucoma, Optical Coherence Tomography, Ophthalmology

Abstract

Background: Retinal diseases such as diabetic retinopathy, age-related macular degeneration, and glaucoma are major causes of vision loss and blindness around the world. Detecting these conditions early and treating them promptly is key to preventing them from worsening and saving sight. New developments in artificial intelligence (AI), especially in machine learning (ML) and deep learning (DL), now offer better ways to automatically analyze retinal images and help doctors make more accurate diagnoses in eye care.

Methods: A narrative literature review was carried out using the PubMed database to find peer-reviewed studies on artificial intelligence in diagnosing retinal diseases. The review included recent studies that assessed AI algorithms used with retinal imaging methods like fundus photography and optical coherence tomography (OCT).

Results: Multiple studies indicate that AI algorithms can accurately detect retinal diseases. Deep learning models, particularly convolutional neural networks, are effective at identifying features linked to diabetic retinopathy, age-related macular degeneration and glaucoma. Some AI systems for diabetic retinopathy screening have achieved sensitivities and specificities above 90%, which is comparable to experienced ophthalmologists. Furthermore, AI-based analysis of OCT images has shown promise in detecting structural retinal changes and monitoring disease progression.

Conclusions: Artificial intelligence could greatly improve how retinal diseases are diagnosed and screened by allowing fast and accurate analysis of retinal images. Although there are still challenges with data quality, transparency, and clinical use, AI-based diagnostic systems will likely become an important part of future eye care.

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