Abstract
This literature review explores the emerging role of digital twin (DT) technology in ophthalmology, emphasizing its potential to revolutionize personalized medicine. DTs integrate diverse data sources, including genetic, environmental, and real-time patient data, to create dynamic, predictive models that enhance risk assessment, surgical planning, and postoperative care. The review highlights vital case studies demonstrating the application of DTs in improving the early detection and management of diseases such as glaucoma and age-related macular degeneration. While implementing DTs presents challenges, including data integration and privacy concerns, the potential benefits, such as improved patient outcomes and cost savings, position DTs as a valuable tool in the future of ophthalmic care. The review underscores the need for further research to address these challenges and fully realize the potential of DTs in clinical practice.