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Foundation Models Advance Ophthalmic Imaging AI: Review Highlights Milestones and Challenges
A systematic review evaluates five cutting-edge ophthalmic AI foundation models, highlighting their technical evolution and clinical trial evidence.
Key Details
- 1Five foundation models reviewed: RETFound, VisionFM, EyeCLIP, EyeFM, MIRAGE.
- 2Multimodal models now integrate up to 11 imaging modalities and clinical text (EyeCLIP).
- 3EyeFM is the only model with RCT-based clinical validation, improving diagnostic accuracy (92.2% vs. 75.4%) and reducing reporting time by 63.3 seconds.
- 4Other models validated only retrospectively; no multicenter or prospective validations except EyeFM.
- 5RETFound, VisionFM showed promising results in predicting systemic diseases from eye images, but results are associations, not causality.
- 6Current key limitations: lack of demographic diversity, limited prospective validation, and reliance on 2D data slices.
Why It Matters
This review signals a shift from technical model performance to clinical integration and real-world validation—critical steps for adoption in radiology and ophthalmology AI. The findings underscore the need for more robust, prospective, and diverse clinical studies before broad clinical translation.
Source
EurekAlert
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