Insight | Eye Discovery: How AI + multimodal data is redefining the ophthalmic diagnostic paradigm

Imaging Data Advancement: AI enhances the precision of pathological feature extraction across various ophthalmic imaging modalities, such as fundus photography and OCT, for conditions like diabetic retinopathy, glaucoma, and cataracts.
Non-Imaging Data Integration: AI is advancing omics research for precision medicine by analyzing genomics, transcriptomics, proteomics, metabolomics, and electronic health records to provide molecular insights for eye disease management.
Multimodal Fusion Techniques: AI is breaking data silos by integrating different types of data (e.g., imaging and omics) into a comprehensive diagnostic and therapeutic system for a more robust understanding of complex ophthalmic diseases.
Scholarly Value and Clinical Trends: The review emphasizes the success of AI in ophthalmology but notes the need for further research on data standardization and algorithm robustness, aiming for developing spatiotemporal response intelligent models and generalizable databases.

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