Revolutionizing Ophthalmology with AI and Deep Learning

Artificial intelligence (AI) that relies on deep learning (DL) has generated significant worldwide attention in the past few decades. DL has been extensively used in the fields of recognizing images, voice recognition, and language processing. However, its influence on health services is only starting to emerge. DL has been utilized in eye care to analyze fundus images, optical coherence tomographic imaging, and visual fields. It has demonstrated strong reliability for classification in identifying diabetes-associated retinopathy, retinopathy of prematurity, glaucomalike disc, macular edema, and age-associated macular degeneration. DL in ophthalmic images may be used with telecommunication as a potential approach for screening, diagnosing, and tracking significant eye conditions for patients in healthcare and community-based environments. However, some difficulties may arise when using DL in the field of ophthalmology. These obstacles include both clinical and technological aspects, as well as concerns over the interpretability of algorithmic outcomes, legal matters related to medical practice, and the willingness of physicians and patients to accept the use of AI algorithms that operate as “black boxes.” DL has the potential to fundamentally transform the practice of ophthalmic in the decades to come. This chapter presents an overview of the most advanced DL systems used in ophthalmology applications. It also discusses probable difficulties in using these systems in a clinical setting and suggests possible solutions for the future.