A Decade of Progress in Artificial Intelligence for Fundus Image-Based Diabetic Retinopathy Screening (2014-2024): A Bibliometric Analysis

Background/AimsDiabetic 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. MethodsThe study analyzed 1,172 publications from the Web of Science Core Collection database using CiteSpace and Microsoft Excel. The analysis included publication trends, citation patterns, institutional collaborations, and keyword emergence analysis. ResultsPublications showed consistent growth from 2014-2022, with a peak in 2021. India (26%), China (20.05%), and USA (9.98%) dominated research output. IEEE ACCESS was the leading publication venue with 44 articles. Research evolved from traditional image processing to deep learning approaches, with recent emphasis on multimodal AI models. The analysis identified three distinct phases: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). ConclusionThe field shows mature development in traditional AI approaches while transitioning toward multimodal learning technologies. Future directions indicate increased focus on telemedicine integration, innovative AI algorithms, and real-world implementation. What is already known on this topicO_LIAI-based DR screening has been developing since the 1960s, with significant acceleration after 2014 due to deep learning advances. C_LIO_LITraditional manual analysis of fundus images is time-consuming and error-prone. C_LI What this study addsO_LIComprehensive mapping of research evolution in AI-based DR screening over the past decade. C_LIO_LIIdentification of research concentration in specific geographical areas and emerging trends in multimodal AI approaches. C_LI How this study might affect research, practice or policyO_LIHighlights the need for increased international collaboration and technology sharing. C_LIO_LISuggests focus areas for future research, including multimodal learning and real-world implementation. C_LIO_LIProvides direction for healthcare organizations and researchers in adopting and developing AI-based DR screening technologies. C_LI