ReviewDiabetes, metabolic syndrome and obesity : targets and therapy2025
A Scoping Review of Artificial Intelligence-Based Health Education Interventions for Patients with Type 2 Diabetes.
Review in Diabetes, metabolic syndrome and obesity : targets and therapy, 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
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
2 citing papers in PubMed.
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- Unlocking AI Chatbot Potential in Healthcare: Trust-Enhanced DeLone & McLean IS Success Model.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Type 2 diabetes mellitus (T2DM) poses a critical global health burden, requiring effective health education to enhance patient self-management. Artificial intelligence (AI) offers personalized and scalable solutions; however, comprehensive syntheses of its applications in T2DM health education are scarce. Objective: Guided by the Arksey and O'Malley scoping review framework, this study maps AI-based health education interventions for T2DM by evaluating technologies, effectiveness, and challenges. Methods: Seven academic databases (PubMed, Web of Science, Embase, Scopus, EBSCO, the Cochrane Library, the Joanna Briggs Institute (JBI) Database, and Wiley Online Library) were searched for studies published from 2008 to March 2025, identifying 14 eligible interventional studies involving 32,478 adult T2DM patients receiving AI-based health education. Results: (1) Technological Diversity: Interventions included mobile apps (eg, FoodLens, TRIO system), chatbots, intelligent platforms, and machine learning algorithms, focusing on diet, glucose monitoring, and lifestyle management. (2) Effectiveness: AI interventions enhanced glycemic control, yielding reductions in glycosylated hemoglobin (HbA1c) of up to 2.59%, improved self‑management adherence (60-85%), and produced positive psychological outcomes (eg, increased self‑efficacy); efficacy varied by intervention duration and user engagement. (3) Challenges: Key barriers included technical complexity, low long-term engagement, digital literacy gaps, and data privacy concerns. Conclusion: AI holds substantial potential for T2DM health education via personalized, accessible interventions. Future research should address technological hurdles, prioritize user-centered design, and integrate AI into healthcare systems to ensure sustainability and equity.
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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.