Published date: Sep 16 2024

Journal Title: Journal of Ophthalmic and Vision Research

Issue title: July–Sep 2024, Volume 19, Issue 3

Pages: 354–367

DOI: 10.18502/jovr.v19i3.15893

Hesam Hashemianshhlucky@yahoo.comTranslational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran

Tunde Petot.peto@qub.ac.ukSchool of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health, Queen’s University Belfast, Northern Ireland

Renato Ambrósio Jrdr.renatoambrosio@gmail.comDepartment of Ophthalmology, Federal University the State of Rio de Janeiro (UNIRIO)

Imre Lengyeli.lengyel@qub.ac.ukSchool of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Northern Ireland

Rahele KafiehRaheleh.kafieh@durham.ac.ukDepartment of Engineering, Durham University, United Kingdom

Ahmed Muhammed Nooriahmedalganemy@gmail.com10 School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.

Masoud Khorrami-Nezhadop_khorrami@yahoo.comOptometry department, School of rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.

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.

Keywords: Artificial Intelligence, Ophthalmology, Prognosis, Screening, Treatment

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