Comprehensive Summary
This study utilizes a systematic review approach to explore how Artificial Intelligence (AI) is transforming ophthalmology, particularly in screening, diagnosis, prognosis, and treatment planning for ocular diseases. The research highlights the use of machine and deep learning models, such as Convolutional Neural Networks, which have demonstrated strong performance in diagnosing conditions like diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, and glaucoma. These AI technologies are applied to imaging methods like fundus photography and Optical Coherence Tomography, automating tasks such as classification, segmentation, and disease staging with high sensitivity and specificity. The study underscores the potential of AI to enhance diagnostic accuracy, expand screening capacity, and assist in treatment planning, thereby optimizing clinical workflows and outcomes.
Outcomes and Implications
The research is significant as it emphasizes the role of AI in improving ophthalmic care, addressing the increasing demand for services. Clinically, AI enhances diagnostic precision, broadens screening capabilities, and aids in treatment planning, contributing to more personalized care. However, the study also points out ethical challenges, such as the lack of diversity in datasets, which need to be addressed to ensure equitable AI applications. Overall, AI holds promise in supporting clinicians and improving patient outcomes in ophthalmology.