ReviewFrontiers in public health2025
A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy.
Review in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness among the working-age population, and its management is challenged by the disease's inherent heterogeneity. Current management paradigms, based on standardized grading, are inadequate for addressing the significant inter-patient variability in disease progression and treatment response, thereby limiting the implementation of personalized medicine. While artificial intelligence (AI) has achieved breakthroughs in unimodal analysis of retinal images, the single dimension of information fails to capture the complete, complex pathophysiology of DR. Against this backdrop, multimodal AI, capable of integrating heterogeneous data from multiple sources, has garnered widespread attention and is regarded as a revolutionary tool to overcome current bottlenecks and achieve a panoramic understanding for the management of each patient. This review aims to systematically explore the frontier research and developmental potential of multimodal AI in DR management. It focuses on its data sources, core fusion technologies, and application framework across the entire management workflow. Furthermore, this review analyzes future challenges and directions, with the goal of providing a theoretical reference and guidance for the advancement of precision medicine in DR.
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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.