Background: Diabetic retinopathy (DR) is a top leading cause of blindness worldwide, requiring early detection for timely intervention. Artificial Intelligence (AI) has emerged as a promising tool to improve DR screening efficiency, accessibility, and cost-effectiveness. This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening. Methods: A systematic review of studies published before September 2024 was conducted throughout PubMed, Scopus, Embase, the Cochrane Library, the National Health Service Economic Evaluation Database, and the Cost-Effectiveness Analysis Registry. Eligible studies were included if they were (1) conducted among type 1 diabetes mellitus or type 2 diabetes mellitus  adult diabetic population; (2) studies compared AI-based DR screening strategy to non-AI screening; and (3) performed a cost-effectiveness analysis. Meta-analysis was applied to pool incremental net benefit (INB) across studies stratified by country income and study perspective using a random-effects model. Statistical heterogeneity among studies was assessed using the I² statistic, Cochrane Q statistics, and meta regression. Results: Nine studies were included in the analysis. From a healthcare system/payer perspective, AI-based DR screening was significantly cost-effective compared to non-AI-based screening, with a pooled INB of 615.77 (95% confidence interval [CI]: 558.27, 673.27). Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries (INB = 613.62, 95% CI: 556.06, 671.18) and upper-/lower- middle income countries (INB = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), though the result lacked statistical significance and showed high heterogeneity. Conclusions: AI-based DR screening is generally cost-effective from a healthcare system perspective, particularly in high-income countries. Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations, to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.

Background: Diabetic retinopathy (DR) is a top leading cause of blindness worldwide, requiring early detection for timely intervention. Artificial Intelligence (AI) has emerged as a promising tool to improve DR screening efficiency, accessibility, and cost-effectiveness. This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening. Methods: A systematic review of studies published before September 2024 was conducted throughout PubMed, Scopus, Embase, the Cochrane Library, the National Health Service Economic Evaluation Database, and the Cost-Effectiveness Analysis Registry. Eligible studies were included if they were (1) conducted among type 1 diabetes mellitus or type 2 diabetes mellitus  adult diabetic population; (2) studies compared AI-based DR screening strategy to non-AI screening; and (3) performed a cost-effectiveness analysis. Meta-analysis was applied to pool incremental net benefit (INB) across studies stratified by country income and study perspective using a random-effects model. Statistical heterogeneity among studies was assessed using the I² statistic, Cochrane Q statistics, and meta regression. Results: Nine studies were included in the analysis. From a healthcare system/payer perspective, AI-based DR screening was significantly cost-effective compared to non-AI-based screening, with a pooled INB of 615.77 (95% confidence interval [CI]: 558.27, 673.27). Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries (INB = 613.62, 95% CI: 556.06, 671.18) and upper-/lower- middle income countries (INB = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), though the result lacked statistical significance and showed high heterogeneity. Conclusions: AI-based DR screening is generally cost-effective from a healthcare system perspective, particularly in high-income countries. Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations, to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.

Background: Diabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods: The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database. The analysis included publication trends over time, citation patterns, institutional collaborations, and the emergence of keywords. Results: From 2014-2022, there was a steady increase in the number of publications, reaching a peak in 2021. India (26%), China (20.05%), and the USA (9.98%) were the major contributors to research output in this field. Among the publication venues, IEEE ACCESS stood out as the leading one, with 44 articles published. The research landscape has evolved from traditional image processing techniques to deep learning approaches. In recent years, there has been a growing emphasis on multimodal AI models. The analysis identified three distinct phases in the development of AI-based DR screening: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). Conclusions: The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies. Future directions suggest an increased focus on the integration of telemedicine, innovative AI algorithms, and real-world implementation of these technologies in real-world settings.

Background: Diabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis. Methods: The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database. The analysis included publication trends over time, citation patterns, institutional collaborations, and the emergence of keywords. Results: From 2014-2022, there was a steady increase in the number of publications, reaching a peak in 2021. India (26%), China (20.05%), and the USA (9.98%) were the major contributors to research output in this field. Among the publication venues, IEEE ACCESS stood out as the leading one, with 44 articles published. The research landscape has evolved from traditional image processing techniques to deep learning approaches. In recent years, there has been a growing emphasis on multimodal AI models. The analysis identified three distinct phases in the development of AI-based DR screening: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). Conclusions: The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies. Future directions suggest an increased focus on the integration of telemedicine, innovative AI algorithms, and real-world implementation of these technologies in real-world settings.