AI-assisted multimodal imaging for early detection of uveal melanoma

Early detection of uveal melanoma (UM) remains a critical determinant of survival, as delayed diagnosis increases metastatic risk and limits therapeutic options. Recent advances in multimodal ocular imaging—such as optical coherence tomography (OCT), fundus autofluorescence (FAF), ultrasonography, and widefield imaging—have significantly enhanced the structural and functional assessment of choroidal lesions. Integrating these modalities with artificial intelligence (AI) is emerging as a transformative approach to improve diagnostic accuracy, reduce observer variability, and enable automated risk stratification. This article reviews the current landscape of AI-enabled multimodal imaging for early UM detection, highlights key algorithmic developments, examines predictive performance across imaging platforms, and discusses challenges related to dataset heterogeneity, model generalizability, and clinical deployment. The synthesis underscores the potential of AI-driven imaging workflows to augment early detection pathways and support precision ocular oncology.