Beyond human perception: Revolutionizing ophthalmology with artificial intelligence and deep learning

Abstract

The purpose of the study was to provide a comprehensive overview of the transformative applications of artificial intelligence (AI) in ophthalmology, with a focus on its impact on screening, diagnosis, and treatment planning. A comprehensive literature search was conducted to identify relevant studies on the applications of AI in ophthalmology. PubMed, Embase, and Scopus were searched using appropriate keywords, with inclusion criteria focusing on studies related to image analysis, diagnostic algorithms, predictive models, and treatment planning. Limited to English-language articles, both original research and review articles were considered, while studies emphasizing nonophthalmic applications of AI or lacking sufficient detail were excluded. AI algorithms, powered by deep learning models, have demonstrated remarkable accuracy in the automated screening and detection of various ocular diseases. The potential implications of AI include revolutionizing screening programs for early identification of individuals at risk, facilitating timely interventions, and improving patient outcomes. The integration of AI with teleophthalmology and remote monitoring systems has the potential to alleviate the burden on health-care systems, particularly in underserved areas. The applications of AI in ophthalmology hold significant potential for transforming the field by enhancing diagnostic accuracy, optimizing treatment strategies, and increasing access to eye care. However, successful implementation requires addressing challenges such as diverse and representative datasets, ensuring interpretability and explainability of AI models, and addressing ethical considerations related to patient privacy and data security. Collaborative efforts between ophthalmologists, data scientists, and regulatory bodies are deemed crucial to fully leverage the potential of AI in ophthalmology.