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This survey reviews deep learning methods for multimodal ophthalmic diagnostics, covering task-specific approaches and large-scale foundation models. It examines how these methods leverage modalities like color fundus photography and optical coherence tomography for tasks like lesion detection and disease diagnosis. The survey highlights key datasets, evaluation metrics, and methodological innovations like self-supervised learning, while also discussing challenges like data variability and limited interpretability, and proposes future directions including ultra-widefield imaging and reinforcement learning.
Multimodal ophthalmic AI is poised for a leap, but current models still struggle with data variability, limited annotations, and generalization across diverse patient populations.
Visual impairment represents a major global health challenge, with multimodal imaging providing complementary information that is essential for accurate ophthalmic diagnosis. This comprehensive survey systematically reviews the latest advances in multimodal deep learning methods in ophthalmology up to the year 2025. The review focuses on two main categories: task-specific multimodal approaches and large-scale multimodal foundation models. Task-specific approaches are designed for particular clinical applications such as lesion detection, disease diagnosis, and image synthesis. These methods utilize a variety of imaging modalities including color fundus photography, optical coherence tomography, and angiography. On the other hand, foundation models combine sophisticated vision-language architectures and large language models pretrained on diverse ophthalmic datasets. These models enable robust cross-modal understanding, automated clinical report generation, and decision support. The survey critically examines important datasets, evaluation metrics, and methodological innovations including self-supervised learning, attention-based fusion, and contrastive alignment. It also discusses ongoing challenges such as variability in data, limited annotations, lack of interpretability, and issues with generalizability across different patient populations. Finally, the survey outlines promising future directions that emphasize the use of ultra-widefield imaging and reinforcement learning-based reasoning frameworks to create intelligent, interpretable, and clinically applicable AI systems for ophthalmology.
