Enhancing ophthalmic diagnosis and treatment with artificial intelligence

Journal article

Olawade, D., Weerasinghe, Kusal, Mathugamage, Mathugamage Don Dasun Eranga, Odetayo, A., Aderinto, Nicholas, Teke, J. and Boussios, S. 2025. Enhancing ophthalmic diagnosis and treatment with artificial intelligence. Medicina (Kaunas, Lithuania). 61 ((3)).
AuthorsOlawade, D., Weerasinghe, Kusal, Mathugamage, Mathugamage Don Dasun Eranga, Odetayo, A., Aderinto, Nicholas, Teke, J. and Boussios, S.
AbstractThe integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings.
KeywordsGlaucoma; Age-related macular degeneration; Ophthalmology; Machine learning; Eye diseases; Diabetic retinopathy; Macular degeneration; Algorithms; Artificial intelligence (AI)
Year2025
JournalMedicina (Kaunas, Lithuania)
Journal citation61 ((3))
PublisherMDPI
ISSN1648-9144
Official URLhttps://www.mdpi.com/1648-9144/61/3/433#:~:text=AI%2Dpowered%20screening%20programs%20are,accuracy%20%5B8%2C87%5D.
Publication dates
Online28 Feb 2025
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Accepted26 Feb 2025
Deposited19 May 2025
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Open
Output statusPublished
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