Comprehensive Summary
This paper comprehensively examines the utilization of artificial intelligence (AI) techniques in ophthalmology and their prospective research trajectories. The research was performed by conducting a comprehensive review of surveys published between 2015 and 2022 across databases including PubMed/Medline, ISI Web of Knowledge, ScienceDirect, and Scopus using specific keywords related to AI in ophthalmology and medicine. From an initial 1762 results, 70 articles and review papers were analyzed after applying inclusion and exclusion criteria. The findings indicate remarkable benefits of AI tools, such as artificial neural networks (ANNs), in evaluating visual field, optic nerve, and retinal nerve fiber layer, leading to higher precision in detecting advances in glaucoma and retinal changes in diabetes. The review identified frequent AI applications in diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD). Main points from the discussion highlight the use of AI for primary diagnosis based on image analysis and its potential for predicting medical events and assessing prognosis. The growth of AI-based technologies, particularly in cataract surgery, is also noted as prominent for the near future.
Outcomes and Implications
This research is important as AI potentially increases patient access to screening and clinical diagnosis and decreases healthcare costs, especially for high-risk populations or communities with financial shortages. AI tools, particularly ANNs, are progressively involved in detecting and customizing control of ophthalmic diseases, with studies analyzing their efficiency in diagnosing and predicting disease evolution. AI systems designed to detect more than mild diabetic retinopathy and diabetic macular edema have been approved by the US FDA for use as a substitute for traditional screening methods, indicating current clinical relevance. While some AI models for predicting glaucoma progression achieve high accuracy in testing, challenges remain for global implementation, such as standardizing databases and addressing financial limitations. in less developed countries, and the ultimate medical recommendations will continue to be made by the clinician