Artificial intelligence is rapidly transforming ophthalmic diagnosis by enabling advanced analysis of clinical information, medical images, and patient data. AI-based technologies are increasingly being explored to support the identification of ocular abnormalities, improve diagnostic consistency, and facilitate earlier recognition of vision-threatening conditions. These developments are creating new possibilities for integrating intelligent technologies into routine ophthalmic practice.
This session will focus on the application of machine learning, deep learning, computer vision, and intelligent diagnostic systems across different areas of ophthalmology. Discussions will examine AI-assisted detection and classification of retinal diseases, glaucoma, corneal disorders, optic nerve conditions, and other ophthalmic abnormalities, with emphasis on the clinical relevance of emerging technologies.
The session will also address the challenges associated with implementing AI in real-world clinical environments, including algorithm validation, interpretability, data quality, interoperability, and clinical workflow integration. Experts will explore how responsible and evidence-based adoption of AI can complement clinical expertise and contribute to more efficient, accurate, and patient-centered vision care.