Artificial Intelligence in Uveitis: Where Are We on the Translational Pathway?

Artificial intelligence (AI) has transformed several areas of ophthalmic practice, most notably diabetic retinopathy screening, where autonomous systems have achieved regulatory approval and real-world deployment. Uveitis, by contrast, remains at an earlier stage in translation. Its heterogeneity, longitudinal disease course and reliance on multimodal clinical reasoning present distinct challenges, compounded by small, fragmented and geographically restricted datasets. This review examines the landscape of AI applications in uveitis and maps them to the established ophthalmic AI validation pipeline, from algorithm development through to clinical deployment. Evidence varies between individual studies: selected vitritis detection and aetiological classification models have undergone independent external testing, while anterior chamber inflammation quantification includes small single-centre feasibility studies. Emerging applications, including recurrent-course prediction, treatment-response modelling, fluorescein angiography segmentation, natural language processing and foundation model fine-tuning, remain predominantly at proof-of-concept stages. Despite encouraging algorithmic performance, the studies discussed have not demonstrated clinical actionability: evidence that an AI output changes a clinical decision, leads to an appropriate management intervention and ultimately improves a patient-relevant outcome. We argue that the principal translational bottleneck is therefore not algorithmic capability alone, but the absence of harmonised longitudinal datasets, geographically diverse validation, prospective evaluation and, critically, studies linking AI output → clinical decision → management intervention → patient outcome. International collaborative registries offer a pathway towards addressing these gaps. The next phase of uveitis AI must move beyond model performance towards clinically actionable systems that improve diagnosis, disease monitoring, treatment selection and relapse prediction, ultimately delivering meaningful benefit to patients with ocular inflammatory disease.