ArticleJournal of diabetes science and technology2026
Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7.
Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- Applications, Technical Characteristics, and Evaluation Approaches of Retrieval-Augmented Generation in Nursing: Scoping Review.Journal of medical Internet research · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers new opportunities to scale and personalize diabetes self-management education and support (DSMES). This study evaluated the quality, accuracy, and reproducibility of DSMES plans generated by ChatGPT-5 using the ADCES7 Self-Care Behaviors™ framework.
methodsEleven virtual patient profiles representing five key DSMES time points (diagnosis, annual review, complicating factors, transitions, and ongoing care) were analyzed using structured prompts with retrieval-augmented generation. Generated plans were assessed using a DSMES Process Evaluation Checklist integrating IMPACTS, QAMAI, PDQI-9, and Donabedian's frameworks across ten domains. Content validity was confirmed by five experts (S-CVI: 0.982 for appropriateness; 0.964 for clarity). Ten certified diabetes educators (mean age 47.9 years; mean experience 14 years) independently scored the plans. Internal consistency, inter-rater reliability, and reproducibility were examined.
resultsMean domain scores ranged from 29.0 ± 2.0 (patient engagement; 58.0%) to 42.7 ± 2.0 (cultural and linguistic appropriateness; 85.5%). ChatGPT-5 demonstrated strengths in clarity (85.1%) and goal-directedness (81.1%), generating evidence-based plans within three to five minutes, but showed limitations in patient engagement. Moderate performance was observed for accuracy (74.9%), relevance (78.7%), individualization (79.6%), and feasibility (75.3%). Reliability was high (Cronbach's α = 0.889; intra-class correlation coefficient = 0.837). Reproducibility was moderate, with intra-assay coefficients of 0.04 to 0.15 and inter-assay coefficients of 0.10 to 0.27.
conclusionsThe DSMES process evaluation checklist is valid and reliable. ChatGPT-5 shows promise as a scalable DSMES decision-support tool, though improvements in personalization, patient engagement, and reproducibility are needed before broader implementation.
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