Comprehensive Summary
This paper offered a comprehensive examination of artificial intelligence (AI) applications, focusing on machine learning (ML) and deep learning (DL), across various fields in ophthalmology, such as glaucoma, corneal issues, and oculoplastics. Highlighted throughout the paper are AI systems, particularly convolutional neural networks (CNNs), that have demonstrated high sensitivity and specificity in identifying glaucomatous changes in optic nerve imaging, classifying corneal pathologies from topography data, and analyzing periocular features for oculoplastic assessments. Several studies showed imaging methods like optical coherence tomography (OCT) and fundus imaging are near expert-level performance. The article continues to discuss both the promising diagnostic precision of these models and the different regulatory and ethical challenges that may hinder clinical implementation, such as a scarcity of datasets, bias, and lack of external validation.
Outcomes and Implications
This research is relevant considering a growing global demand for eye care services and a lack of trained ophthalmic professionals, particularly in underserved geographical areas. AI-enabled tools could substantially improve early identification of glaucoma and corneal diseases and assist in oculoplastic diagnosis, further decreasing clinical burden and increasing access to timely diagnosis and treatment. Additionally, AI use in patient triage and screening could facilitate low-cost identification of the large-scale demands of patients. By integrating these systems into clinical workflow, care teams could increase consistency of diagnosis, personalize treatment planning, and increase the efficiency of the treatment of patients in both rural and urban healthcare populations. The authors suggest validated and reliable technologies may provide identified improvements in ophthalmic care provision in the next few years and a shift to a more sustainable and accessible healthcare model.