Artificial intelligence (AI) is rapidly transforming ophthalmology, particularly in image-based screening, diagnosis, and disease management.
This whitepaper explores how machine learning and deep learning algorithms are being applied across key ophthalmic conditions, including diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and cataracts. It examines the growing role of AI in fundus photography, OCT imaging, and smartphone-based screening platforms, highlighting real-world performance metrics such as sensitivity, specificity, and scalability.
The paper also addresses practical limitations—data quality, population bias, explainability, and clinical accountability—providing a balanced view of AI’s clinical readiness.
Together, these insights position AI as a powerful tool to augment clinicians, expand access to eye care, and enable earlier, more accurate detection of vision-threatening disease.