Artificial intelligence (AI) is poised to revolutionize ophthalmic imaging by transitioning from narrow, disease-specific systems to versatile foundation models capable of learning from diverse datasets. A review published on October 24, 2025, in Advances in Ophthalmology Practice and Research highlights the potential of these models in improving diagnostic performance for various retinal diseases, including diabetic retinopathy and age-related macular degeneration. Notably, RETFound demonstrated an area under the curve (AUC) of 0.94 for diabetic retinopathy detection, while VisionFM achieved an AUC of 0.974 for age-related macular degeneration. The review also emphasizes the ability of RetiZero to identify over 400 rare fundus diseases with a top-five accuracy of 75.6%. Despite these promising results, the authors caution that the integration of these models into clinical practice requires careful validation, transparency, and the ability to support clinical judgment. They argue that addressing challenges such as algorithmic bias and data diversity is crucial for real-world adoption. The authors advocate for future work to prioritize larger datasets and explainable AI tools to ensure that these models can enhance early diagnosis and improve access to specialized eye care.