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The research problem addresses the need for accurate and efficient detection of retina diseases using artificial intelligence (AI) technologies. The specific aim is to evaluate the performance, ethical considerations, and clinical implementation of AI-driven retina disease detection systems. This study is a systematic review. Data sources assessed included various electronic databases searched up to July 31, 2023. The prespecified criteria for study inclusion were studies involving AI algorithms for retina disease detection, including those focused on diabetic retinopathy, age-related macular degeneration, and glaucoma. Participant eligibility criteria encompassed human subjects of all ages, and the interventions assessed were AI-based diagnostic tools compared to traditional diagnostic methods. Only randomized controlled trials and observational studies set in clinical environments were included, covering a time span from the inception of AI technology. The search identified 145 studies, of which 61 met the inclusion criteria and were eligible for analysis. The narrative summary of findings indicated that AI algorithms generally demonstrated high accuracy, sensitivity, and specificity in detecting retinal diseases. Deep learning algorithms showed a sensitivity of 90 % and specificity of 98 % for diabetic retinopathy detection. However, several studies highlighted concerns about algorithmic bias, data privacy, and the need for diverse and representative datasets to ensure generalizability across different populations. The AI-driven retina disease detection systems have significant potential to improve diagnostic accuracy and efficiency in clinical practice. Ethical considerations regarding patient privacy, the risk of algorithmic bias, and the challenges of integrating AI into existing healthcare workflows must be addressed. The study underscores the importance of ongoing validation, ethical scrutiny, and interdisciplinary collaboration to harness the benefits of AI while mitigating its risks, ensuring responsible and equitable implementation in clinical settings.
Fatima et al. (Fri,) studied this question.