Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.

Machine learning and deep learning models show strong diagnostic performance and emerging prognostic value in uveal melanoma. However, reproducibility and real-world validation remain limited.

New foundation models such as RETFound and VisionFM, trained on large multimodal eye datasets, could improve standardization, explainability, and cross-center generalization.

As these models evolve, they have the potential to become essential tools in clinical practice, accelerating the translation of AI into reliable, routine implementation in ocular oncology.