An overview of AI applications in ophthalmology

A new review article published in Photodiagnosis and Photodynamic Therapy has outlined key publication trends in artificial intelligence (AI) research within ophthalmology over the past two decades.

The review found an “exponential growth” in the research topic after 2015, with deep learning, optical coherence tomography (OCT), and diabetic retinopathy emerging as dominant themes.

Researchers highlighted that between 2010 and 2015, academic publications combining AI and ophthalmology focused on basic technologies – such as machine learning, algorithms, OCT, and neural networks – and on disease-specific interests, such as glaucoma.

Between 2016 and 2020, research concentrated on “more practical themes” including segmentation and expanded uses of OCT.

Between 2021 and 2024, additional key research themes developed among the research, such as therapeutic monitoring.

The scientists highlighted that the thematic trends in research over the two decades illustrate: “the transformation of the field from developing algorithms to clinically integrated AI solutions in ophthalmology, as a sign of a maturing and diversifying research base.”

Within AI and ophthalmology research, the study authors listed a series of “prolific entities” including the University of London (with 202 publications on the topic), the Medical University Vienna and the National University of Singapore (with 191 publications each).

The authors observed that despite strong growth in the research field, notable gaps remain in “real-world clinical integration, regulatory frameworks, and representation from low-resource regions.”

“This study not only maps the current intellectual terrain of AI in ophthalmology but also identifies critical avenues for future research to ensure equitable, interpretable, and clinically translatable AI solutions in eye care,” the researchers shared.