Introduction: Artificial intelligence (AI) is reshaping diagnostic paradigms across oncology. In ophthalmic oncology encompassing conditions like retinoblastoma and uveal melanoma, AI has immense potential due to the specialty's reliance on advanced imaging and the importance of early and accurate diagnosis.
Areas covered: This review explores recent developments in AI applications for ophthalmic oncology, particularly in imaging-based detection, prognostication, and treatment planning. A structured search of PubMed and Web of Science from 2010-2025 identified key studies focusing on AI's use in diagnosing retinoblastoma from fundus photography and differentiating uveal melanoma from benign lesions. Additionally, AI models trained on multimodal inputs such as Optical Coherence Tomography, Ultrasound B scan, and histopathology have demonstrated promise in outcome prediction and therapy stratification.
Expert opinion: AI algorithms have achieved diagnostic performance comparable to ophthalmic oncology experts and, in some cases, outperformed them in detecting subtle malignancies. While promising, clinical adoption remains limited by the need for large, diverse datasets and prospective validation. Multicenter collaborations and integration of multimodal imaging are essential to move from proof-of-concept to real-world application. With continued development, AI can significantly enhance diagnostic precision, personalize care, and serve as a scalable model for cancer imaging in ophthalmology and beyond.
Keywords: Artificial intelligence; biomedical imaging; deep learning; ocular oncology; retinoblastoma; uveal melanoma.