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

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.