A Scoping Review of Artificial Intelligence in Ocular Oncology.

Objective

To provide a comprehensive literature review of original work on artificial intelligence in ocular oncology.

Methods

Scoping review of PubMed-indexed original articles (n = 94) on the use of artificial intelligence in ocular oncology, retrieved during the month of February 2026 and independently screened by two ocular oncologists.

Results

Most of the literature on artificial intelligence (AI) in ocular oncology focuses on uveal melanoma and its differentials (n = 39, 41%), followed by retinoblastoma (n = 14, 15%) and orbital tumors (n = 12, 13%). The purpose of using the AI models was to screen, diagnose, and classify the disease (n = 59, 62%) and to treat, predict outcomes, and monitor the disease (n = 35, 37%). Most literature (n = 32, 34%) on AI in ocular oncology originates from China. Datasets comprised images in 78% (n = 73) of the studies, clinical parameters in 14% (n = 13), and omics data in 12% (n = 11). Most studies worked on developing AI models (n = 83, 88%), of which two reached a deployment stage. Few studies evaluated or incorporated pre-existing models (n = 11, 12%). Supervised learning strategy was most commonly employed (n = 75, 80%). Among studies that developed AI models, traditional machine learning architectures were used in 36, deep learning in 39, and a combination in 8. Most studies (n = 59, 63%) were at a Clinical AI Readiness Evaluator Technology Readiness Level 4, i.e., at the prototype development stage.

Conclusions

Despite the limitation of a single database search, a surge in AI applications in ocular oncology after 2020 is evident. Most studies are in the model development stage, and few have been deployed in the real world for clinical implementation. Very few models have proven effective in real-world clinics and the community, holding promise for the future.