Clinical Report: Building on AI Foundations
Overview
A systematic review highlights the potential of vision and vision-language foundation models in ophthalmology. However, challenges such as bias, interpretability, and integration into clinical practice remain significant barriers.
Background
The integration of artificial intelligence (AI) in healthcare, particularly in ophthalmology, is increasingly relevant due to the specialty's reliance on image-based diagnostics. Foundation models, trained on extensive multimodal datasets, offer the potential for improved diagnostic accuracy across various eye diseases.
Data Highlights
| Model | Condition | AUC |
|---|---|---|
| RETFound | Diabetic Retinopathy | 0.94 |
| VisionFM | Age-related Macular Degeneration | 0.974 |
| VisionFM | Diabetic Retinopathy | 0.945 |
| Glaucoma Models | Glaucoma | 0.721 - 0.913 |
| OSPM | Ocular Surface Tumor | 0.993 |
Key Findings
- Foundation models can adapt across multiple diagnostic tasks with limited labeled data.
- Models demonstrated performance approaching or exceeding that of experienced clinicians.
- Multimodal AI models like EyeCLIP and MetaGP improve diagnostic reasoning by integrating imaging with clinical data.
- Some models showed strong performance in few-shot and zero-shot scenarios, beneficial for rare conditions.
- Concerns about algorithmic bias and generalizability were raised due to reliance on retrospective datasets.
Clinical Implications
Ongoing evaluation of these models is necessary to ensure their safe and effective integration into clinical workflows.
Conclusion
Significant barriers must be addressed before widespread clinical adoption can occur.
Related Resources & Content
- A systematic review of vision and vision-language foundation models in ophthalmology - PubMed, 2026 -- A systematic review of vision and vision-language foundation models in ophthalmology
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections | npj Digital Medicine, 2026 -- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections
- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions | FDA, 2025 -- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions
- the pathologist — AI Training: The Big Picture
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- Journal of Medical Internet Research (JMIR) — Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective
- Frontiers in Medicine — Integrating AI into undergraduate medical education: an exploration of learner-centered approaches through AI
- AI Training: The Big Picture
- AI in Practice
- Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective
- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions | FDA
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections | npj Digital Medicine
- A systematic review of vision and vision-language foundation models in ophthalmology - PubMed
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