Next-Generation Ophthalmology: How Artificial Intelligence Is Shaping the Future of Eye Care

When Canada hosted the inaugural “Artificial Intelligence, Digital Health, and the Eye” conference in April 2023, it quietly launched what has become a defining forum at the intersection of technology and vision science in the country. A year later, the conference found a new stage at the Royal Society of Medicine in London, drawing global attention. This was largely because the keynote address was delivered by Professor Geoffrey Hinton, co-recipient of the 2024 Nobel Prize in Physics, and widely regarded as one of the founding fathers of deep learning. As modern medicine continues to be shaped by artificial intelligence (AI), the tone is unmistakably clear: the future is not only digital, but also intelligent. Few medical specialties are as naturally aligned with AI as ophthalmology. Its high-resolution imaging and quantitative data make ophthalmology particularly well-suited for the integration of AI technologies. Beyond automating image interpretation, AI now holds promise in risk stratification, disease progression modelling, and even in democratizing access to subspecialty-level diagnostics—advances that could meaningfully alter the delivery of eye care across the globe. Significant strides have been achieved in applying both machine learning (ML) and deep learning (DL) algorithms to major ophthalmic diseases, including diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, and various corneal pathologies. The U.S. Food and Drug Administration (FDA) has approved several AI-based platforms for clinical use, many of which are showing tremendous potential. Large language model AI systems, such as Generative Pre-Training–Model 4 (better known as GPT-4 by OpenAI), have demonstrated the ability to either match or outperform human ophthalmologists in diagnosing and treating various ophthalmic diseases.1 This article aims to provide a comprehensive review of the role of AI in ophthalmology, with particular attention to current clinical applications, emerging innovations, and the challenges that lie ahead.

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Publication Details

Journal
Canadian Eye Care Today
Published
2025-06-17
DOI
https://doi.org/10.58931/cect.2025.4153
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

Next-Generation Ophthalmology: How Artificial Intelligence Is Shaping the Future of Eye Care

Michelle Khan
Canadian Eye Care Today
Retinal Imaging and Analysis
article

Next-Generation Ophthalmology: How Artificial Intelligence Is Shaping the Future of Eye Care

Michelle Khan
article en

Abstract

When Canada hosted the inaugural “Artificial Intelligence, Digital Health, and the Eye” conference in April 2023, it quietly launched what has become a defining forum at the intersection of technology and vision science in the country. A year later, the conference found a new stage at the Royal Society of Medicine in London, drawing global attention. This was largely because the keynote address was delivered by Professor Geoffrey Hinton, co-recipient of the 2024 Nobel Prize in Physics, and widely regarded as one of the founding fathers of deep learning. As modern medicine continues to be shaped by artificial intelligence (AI), the tone is unmistakably clear: the future is not only digital, but also intelligent. Few medical specialties are as naturally aligned with AI as ophthalmology. Its high-resolution imaging and quantitative data make ophthalmology particularly well-suited for the integration of AI technologies. Beyond automating image interpretation, AI now holds promise in risk stratification, disease progression modelling, and even in democratizing access to subspecialty-level diagnostics—advances that could meaningfully alter the delivery of eye care across the globe. Significant strides have been achieved in applying both machine learning (ML) and deep learning (DL) algorithms to major ophthalmic diseases, including diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, and various corneal pathologies. The U.S. Food and Drug Administration (FDA) has approved several AI-based platforms for clinical use, many of which are showing tremendous potential. Large language model AI systems, such as Generative Pre-Training–Model 4 (better known as GPT-4 by OpenAI), have demonstrated the ability to either match or outperform human ophthalmologists in diagnosing and treating various ophthalmic diseases.1 This article aims to provide a comprehensive review of the role of AI in ophthalmology, with particular attention to current clinical applications, emerging innovations, and the challenges that lie ahead.

Canadian Eye Care Today
University of Toronto (CA)
Openalex Percentile: Top 79%
Retinal Imaging and Analysis
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