# FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis

arXiv: https://arxiv.org/abs/2508.11721 · HTML: https://mlsanity.com/paper/2508.11721/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [A Multimodal Vision Foundation Model for Clinical Dermatology](https://mlsanity.com/paper/2410.15038/) (2024)
- [VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence](https://mlsanity.com/paper/2310.04992/) (2023)
- [EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging](https://mlsanity.com/paper/2405.11338/) (2024)
- [An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care](https://mlsanity.com/paper/2505.08414/) (2025)
- [Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting](https://mlsanity.com/paper/2409.17332/) (2024)
- [Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?](https://mlsanity.com/paper/2501.12016/) (2025)

## Built on by (most cited first, 3 of 3)

- [Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics](https://mlsanity.com/paper/2509.03421/) (2025)
- [SemiSAM-O1: Pushing the Boundary of Annotation-Efficient Medical Image Segmentation with Generalist Knowledge Fusion](https://mlsanity.com/paper/2604.24109/) (2026)
- [Uncertainty-Aware Last-Layer Adaptation of RETFound for Referable Diabetic Retinopathy Screening Under Dataset Shift](https://mlsanity.com/paper/2607.02569/) (2026)

## Abstract (arXiv)

Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently emerged, but there is still no clear answer to fundamental questions: Which FM performs the best? Are they equally good across different tasks? What if we combine all FMs together? To our knowledge, this is the first study to systematically evaluate both single and fused ophthalmic FMs. To address these questions, we propose FusionFM, a comprehensive evaluation suite, along with two fusion approaches to integrate different ophthalmic FMs. Our framework covers both ophthalmic disease detection (glaucoma, diabetic retinopathy, and age-related macular degeneration) and systemic disease prediction (diabetes and hypertension) based on retinal imaging. We benchmarked four state-of-the-art FMs (RETFound, VisionFM, RetiZero, and DINORET) using standardized datasets from multiple countries and evaluated their performance using AUC and F1 metrics. Our results show that DINORET and RetiZero achieve superior performance in both ophthalmic and systemic disease tasks, with RetiZero exhibiting stronger generalization on external datasets. Regarding fusion strategies, the Gating-based approach provides modest improvements in predicting glaucoma, AMD, and hypertension. Despite these advances, predicting systemic diseases, especially hypertension in external cohort remains challenging. These findings provide an evidence-based evaluation of ophthalmic FMs, highlight the benefits of model fusion, and point to strategies for enhancing their clinical applicability.