Objective:

To evaluate the potential of vision and vision-language foundation models in ophthalmology and identify challenges to their adoption.

Approach:

• Systematic Review: Analyzed 10 studies published between 2023 and 2025 on AI foundation models in retinal disease, glaucoma, and ocular oncology.

Key Findings:

• Foundation models can adapt across multiple diagnostic tasks with limited labeled data.
• Ophthalmology is well-suited for AI due to its reliance on image-based diagnostics.
• Models demonstrated performance nearing or exceeding that of experienced clinicians.
• RETFound achieved an AUC of 0.94 for diabetic retinopathy detection.
• VisionFM reached AUC values of 0.974 for age-related macular degeneration and 0.945 for diabetic retinopathy.
• Models targeting glaucoma detection reported AUC values ranging from 0.721 to 0.913.
• The ocular surface tumor model OSPM achieved AUC scores as high as 0.993.
• Models showed strong results in few-shot and zero-shot scenarios, beneficial for rare conditions.

Interpretation:

Significant challenges remain regarding bias, interpretability, and integration into clinical practice.

Limitations:

• Most studies relied on retrospective datasets and convenience sampling.
• Concerns about generalizability across different populations and imaging devices.
• Algorithmic bias and computational demands pose challenges.
• Fragmented EHR interoperability affects model deployment.
• The 'black-box' nature of deep learning complicates understanding model predictions.

Conclusion:

Foundation models may enable scalable, multimodal AI systems for diagnosis and decision-making.

Sources:

• Advances in Ophthalmology Practice and Research