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Foundation models for ophthalmic imaging.

Kayvan Gharbi1, Peter van Wijngaarden2, Xavier Hadoux1

  • 1Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia.

Survey of Ophthalmology
|January 31, 2026
PubMed
Summary
This summary is machine-generated.

Ophthalmic foundation models are evolving from specialized to versatile AI tools. Recent advancements focus on integrating diverse data and text guidance for improved eye care applications.

Keywords:
Clinical AIContrastive learningFoundation modelsMasked autoencodersMultimodal imagingOphthalmic diagnosticsOphthalmologyRetinal imagingSelf-supervised learningVision-language models

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Area of Science:

  • Ophthalmic Artificial Intelligence
  • Machine Learning in Healthcare
  • Medical Imaging Analysis

Background:

  • Foundation models offer transferable feature learning from large, unlabeled ophthalmic datasets.
  • These models are crucial for advancing artificial intelligence in ophthalmology.
  • They enable flexible application across various downstream diagnostic and analytical tasks.

Purpose of the Study:

  • To systematically analyze the evolution of ophthalmic foundation models from 2022 to July 2025.
  • To examine key advancements in model architecture, pre-training, and supervision strategies.
  • To provide a comprehensive overview of current trends and future directions in the field.

Main Methods:

  • Systematic review of 12 distinct ophthalmic foundation models.
  • Analysis of modality integration (unimodal, multimodal, vision-language).
  • Evaluation of pretraining objectives (generative vs. contrastive) and supervision (image/text-guided).

Main Results:

  • Observed a shift from domain-specific unimodal models to modality-agnostic models guided by clinical text.
  • Emerging techniques like modality-agnostic encoders and synthetic data augmentation enhance performance and generalizability.
  • Advances in pretraining and supervision strategies are key drivers of progress.

Conclusions:

  • Ophthalmic foundation models are increasingly sophisticated, moving towards broader applicability.
  • Future research should focus on wider modality integration, higher-dimensional data, and standardized benchmarks.
  • This review provides foundational knowledge for developing and applying these AI models in ophthalmology.