ReviewFrontiers in endocrinology2026
Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).
Review in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Objective: 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. Methods: Following a systematic search and screening process, 12,741 publications were identified from the Results: 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. Conclusion: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.