ArticleMedicine2026
A bibliometric analysis of global trends in AI-driven digital health technologies for diabetes management.
Article in Medicine, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions.
methodsWe collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords.
resultsA total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." "diabetes" was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening.
conclusionsThis study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.
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.