Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment

Artificial Intelligence (AI) has undoubtedly emerged as a transformative technology in the field of medicine. In ophthalmology, it has been a catalyst for innovation in the methods used for the diagnosis, management, and treatment of different eye diseases. This article offers a detailed review of the literature on the application and utilization of AI technology in the field of ophthalmology. A detailed search of available literature on the use of AI in the field of ophthalmology was performed through the PubMed database and Google Scholar. Published literature on the role of AI in screening, diagnosis, and management of common ocular conditions such as diabetic retinopathy (DR), cataract, glaucoma, and age-related macular degeneration (AMD) was reviewed. Special emphasis was laid on the effectiveness and limitations of the recently developed AI systems for the detection and management of ocular conditions. We screened (n=4449) records and included (n=102) studies spanning retina, glaucoma, cornea, pediatric ophthalmology, neuro-ophthalmology, ocular oncology, surgery, emergencies, and tele-ophthalmology. Deep learning (DL) and machine learning (ML) algorithms have demonstrated significant performance in the analysis of ophthalmic data, including optical coherence tomography scans and retinal images, for accurately diagnosing and classifying diseases, predicting disease progression, and personalizing different treatment plans. In addition to the common ocular conditions, the use of AI has now spread to other domains of ophthalmology, such as pediatric ophthalmology, oculoplastics and reconstructive surgery, and triage and management of emergency ocular conditions. Various AI systems have shown accuracy similar to that of clinical experts, with the additional benefit of being less subjective and time-consuming. Despite significant progress, different challenges related to regulatory approval, standardization, data quality, and ethical considerations hamper the wide-scale implementation of AI in ophthalmology. Literature is evident on the transformative role of AI in screening, diagnosis, and management of various ocular conditions. However, currently, there are various challenges and limitations to the implementation of AI. Future research should focus on addressing these challenges while optimizing the utilization of AI algorithms for enhancing patient care in ophthalmology.