Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review Article Swipe
Hesam Hashemian
,
Tünde Pető
,
Renato Ambrósio
,
Imre Lengyel
,
Rahele Kafieh
,
Ahmed Muhammed Noori
,
Masoud Khorrami‐Nejad
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jovr.v19i3.15893
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jovr.v19i3.15893
Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such as machine learning and deep learning to major eye diseases. In diabetic retinopathy, AI algorithms analyze retinal images to accurately identify lesions, which helps clinicians in ophthalmology practice. Systems like IDx- DR (IDx Technologies Inc, USA) are FDA-approved for autonomous detection of referable diabetic retinopathy. For glaucoma, deep learning models assess optic nerve head morphology in fundus photographs to detect damage. In age-related macular degeneration, AI can quantify drusen and diagnose disease severity from both color fundus and optical coherence tomography images. AI has also been used in screening for retinopathy of prematurity, keratoconus, and dry eye disease. Beyond screening, AI can aid treatment decisions by forecasting disease progression and anti- VEGF response. However, potential limitations such as the quality and diversity of training data, lack of rigorous clinical validation, and challenges in regulatory approval and clinician trust must be addressed for the widespread adoption of AI. Two other significant hurdles include the integration of AI into existing clinical workflows and ensuring transparency in AI decisionmaking processes. With continued research to address these limitations, AI promises to enable earlier diagnosis, optimized resource allocation, personalized treatment, and improved patient outcomes. Besides, synergistic human-AI systems could set a new standard for evidence-based, precise ophthalmic care.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.18502/jovr.v19i3.15893
- OA Status
- gold
- Cited By
- 18
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402865623Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18502/jovr.v19i3.15893Digital Object Identifier
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Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive ReviewWork title
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reviewOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-09-16Full publication date if available
- Authors
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Hesam Hashemian, Tünde Pető, Renato Ambrósio, Imre Lengyel, Rahele Kafieh, Ahmed Muhammed Noori, Masoud Khorrami‐NejadList of authors in order
- Landing page
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https://doi.org/10.18502/jovr.v19i3.15893Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.18502/jovr.v19i3.15893Direct OA link when available
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Medicine, Artificial intelligence, Retinopathy of prematurity, Glaucoma, Diabetic retinopathy, Macular degeneration, Fundus (uterus), Machine learning, Optometry, Drusen, Deep learning, Medical physics, Ophthalmology, Computer science, Biology, Genetics, Diabetes mellitus, Gestational age, Pregnancy, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 17, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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