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