Systematic Reviews on the Use of Artificial Intelligence in Eye Care Management

This systematic review discusses the benefits, difficulties, and prospects of artificial intelligence (AI) in eye health services, within the scope of diagnostic, therapeutic, and operational functions.A thorough search for pertinent literature conducted across several databases, namely, PubMed, Scopus, and IEEE Xplore identified articles published from 2019 to the present.Studies exploring the applications of AI in terms of diagnostic accuracy and treatment outcomes, the integration of technology in the workflows and consistency and bias were included and rated with metrics like the Cochrane Risk of Bias Tool and PROBAST-AI.The findings suggest that deep learning AI models, such as convolutional neural networks, provide high often greater than 90% diagnostic accuracy in retinal diseases like diabetic retinopathy and age-related macular degeneration, enabling early detection and better treatment planning.The use of AI in eye care also proved to be cost effective especially in those areas with less eye care specialists, as it lessened the demand for specialist input and re-structured service delivery.Nevertheless, several issues limit both generalizability and clinical use, such as lack of diversity in the datasets used, the general inability to explain the decision-making process of AI tools and most studies being observational in nature, which affects the quality of the evidence presented.Solutions to these issues, that is, standardization of datasets and better clarity of the model will be central to the expanded use of the applications.Altogether, while AI has the potential to be transformative in improving eye care preparation and treatment, the existing barriers have to be eliminated to achieve the anticipated benefits clinically.