Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026)

Abstract

Diabetic Retinopathy (DR) is a major global cause of blindness. Artificial Intelligence (AI) has markedly impacted fundus screening over the past three decades, yet systematic bibliometric mapping of the knowledge architecture, evolutionary paths, and synergy among AI models, data modalities, and DR remains scarce. This study conducted a comprehensive bibliometric analysis to characterize the AI-driven DR knowledge structure, collaboration networks, and hotspot migration, and to elucidate co-evolutionary dynamics among AI advances, data modality development, and DR research. Following a systematic search and screening process, 12,741 publications were identified from the Web of Science Core Collection (WoSCC), PubMed, and Scopus, spanning from January 1996 to June 2026. The analytical framework combined multiple methodologies: VOSviewer for collaborative network visualization, CiteSpace for burst detection and timeline mapping, and a Python-based text-mining pipeline for standardized extraction and normalization of AI model names, data modalities, and disease entities. The field exhibits a distinct three-stage evolutionary trajectory: the traditional machine learning era (1996-2014), the deep learning surge (2015-2019), and the current phase marked by the growing prominence of Transformer-based models (2020-present). The collaboration landscape is multipolar, with the United States, China, and India as hubs, while Singapore produces high-impact research. The knowledge base rests on algorithmic innovation and clinical validation. Convolutional neural networks have long served as the backbone architecture in the literature, while Vision Transformers have shown a clear upward trend in publication volume in recent years. Research hotspots are expanding from single-disease classification toward multimodal integration. Although fundus imaging remains the predominant data source, the potential of electronic health record narratives and multi-omics data is increasingly recognized. Overall, the research focus is shifting from “black-box” pattern recognition toward explainable AI and end-to-end clinical translation. This study presents a systematic bibliometric mapping of AI-driven DR research, revealing high-frequency co-occurrence patterns between architectural specialization and clinical demands. Challenges persist in data integration, rare-disease evidence, and cross-setting validation. The future is likely to be shaped by multimodal foundation models and portable acquisition, transitioning AI toward comprehensive clinical decision support.