ArticleJournal of healthcare informatics research2026
Assessing the User Experience of an LLM-Based Conversational Assistant in Diabetes Mellitus Care.
Article in Journal of healthcare informatics research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents the design, implementation, and evaluation of MarIA, a GPT-3.5-powered virtual assistant integrated into a messaging platform to support patients with type 2 diabetes mellitus (DM). MarIA employs a multi-agent architecture that enables varying dialogue styles and degrees of personalization. In a 3-month longitudinal study involving 35 participants, personalized interactions increased engagement by 26%, while message length more than quadrupled-yielding a richer understanding of patient context. This deeper contextualization enabled MarIA to initiate more relevant, meaningful conversations, fostering a positive cycle of sustained engagement. Safety was critically assessed. While MarIA did not generate factual hallucinations, some general health suggestions-though accurate in isolation-could be inappropriate for users with specific clinical constraints. This underscores the need not only for comprehensive patient profiling but also for an embedded safety layer capable of detecting potentially unsuitable recommendations before or even after delivery. The multi-agent architecture proved essential in enabling proactive behaviors, nuanced context detection, and dialogue adaptability, ultimately enhancing both engagement and user safety in AI-supported chronic care. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-025-00217-5.
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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 OpenQuestion graph.