The Advent of Big Data and Machine Learning in Ophthalmology: The 2026 Cogan Lecture

The major objective of this lecture is to summarize the evolution of computational ophthalmology over the past 2 decades through the scientific advances that have transformed ophthalmic research. Topics include the emergence of computational ophthalmology, the role of big data in scientific discovery, the application of machine learning to ophthalmic imaging, and the development of artificial intelligence (AI)-ready data ecosystems. The lecture concludes by exploring the next frontier in computational ophthalmology, highlighting foundation models and agentic AI as emerging approaches with the potential to accelerate scientific discovery, and improve clinical care are discussed. This article is based on the 2026 Cogan Award Lecture presented by Aaron Y. Lee, MD, MSCI, and Cecilia S. Lee, MD, MS, at ARVO 2026.

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Publication Details

Journal
Investigative Ophthalmology & Visual Science
Published
2026-10-28
DOI
https://doi.org/10.1167/iovs.67.12.72
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

The Advent of Big Data and Machine Learning in Ophthalmology: The 2026 Cogan Lecture

Aaron Lee, Scott Song, Cecilia S. Lee
1 links
Investigative Ophthalmology & Visual Science
Retinal Imaging and Analysis
article

The Advent of Big Data and Machine Learning in Ophthalmology: The 2026 Cogan Lecture

Aaron Lee, Scott Song, Cecilia S. Lee
article en

Abstract

The major objective of this lecture is to summarize the evolution of computational ophthalmology over the past 2 decades through the scientific advances that have transformed ophthalmic research. Topics include the emergence of computational ophthalmology, the role of big data in scientific discovery, the application of machine learning to ophthalmic imaging, and the development of artificial intelligence (AI)-ready data ecosystems. The lecture concludes by exploring the next frontier in computational ophthalmology, highlighting foundation models and agentic AI as emerging approaches with the potential to accelerate scientific discovery, and improve clinical care are discussed. This article is based on the 2026 Cogan Award Lecture presented by Aaron Y. Lee, MD, MSCI, and Cecilia S. Lee, MD, MS, at ARVO 2026.

Investigative Ophthalmology & Visual ScienceVol. 67(12)
Washington University in St. Louis (US)
Openalex Percentile: Top 13%
Retinal Imaging and Analysis
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