Economic Evaluations of Artificial Intelligence Implementation in Diabetic Retinopathy Screening
Abstract
Diabetic retinopathy (DR) is a common complication of diabetes that damages retinal blood vessels and can lead to vision impairment. The application of Artificial Intelligence (AI) in DR screening offers a promising alternative to conventional methods. However, further research is crucial to determine the cost-effectiveness of this intervention.
This study systematically reviewed economic evaluations of AI interventions in DR screening using data from PubMed and ScienceDirect (2014–2023). Studies in various healthcare settings assessing cost-effectiveness outcomes, such as incremental cost-effectiveness ratio (ICER) and net monetary benefit, were included. The CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklist was used to assess the reporting quality of included studies.
AI intervention can potentially provide accurate diagnoses by performing complex data analysis quickly and consistently. Despite initial higher costs, AI screening often led to higher quality-adjusted life years (QALYs) and improved healthcare resource allocation, particularly in underserved areas. From several perspectives, AI screening is cost-effective compared to manual screening, which has a lower ICER. Seven out of eight articles concluded that using AI for screening is cost-effective. However, challenges in generalizing AI models across diverse populations suggest a need for further validation to prevent diagnostic bias and ensure healthcare equity. Specifically, the hybrid use of manual screening with AI assistance is more cost-effective than the other comparison methods.
AI can improve diagnoses like DR through quick data analysis and accuracy, but human guidance is still needed for algorithm development and decision-making. Combining AI with human involvement can lead to more cost-effective interventions.
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DOI: https://doi.org/10.15416/pcpr.v9i3.60068
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