ReviewDigital health
Application of chatbots in chronic disease management: A scoping review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- AI-Augmented Teach-Back in Dentistry: From Patient Education to Verified Clinical Understanding.Cureus · 2026Review
- Data-driven closed-loop health education: constructing an integrated assessment-intervention-feedback pathway for digital COPD management-a perspective.Frontiers in public health · 2026Article
- Article
- Review
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: Chatbots have been extensively utilized in chronic disease management to collect real-time health data, deliver personalized educational content, and guide self-management. Nevertheless, critical research gaps persist regarding their differential implementation across specific contexts and quantified comparative effectiveness. Objective: To synthesize existing research on the application of chatbots in chronic disease management, providing evidence-based insights to inform future clinical practice. Methods: Following Arksey and O'Malley's framework, we systematically searched eight databases from their inception until October 20, 2024. Relevant data were extracted from eligible studies, with a focus on disease areas, application platforms, interaction methods, technical architectures, implementation elements, and evaluation indicators. The findings were then synthesized and analyzed to identify key trends and gaps in the literature. Results: A total of 19 studies were included in this review, comprising 10 randomized controlled trials (RCTs) and 9 quasi-experimental studies. The investigated chronic conditions encompassed cancer, diabetes, hypertension, and other prevalent chronic diseases. Chatbot deployment platforms primarily included mobile applications, web-based platforms, and instant messaging software. The underlying technical architectures consisted of artificial intelligence-driven systems, rule-based systems, and hybrid models. The implementation strategies were categorized into night key dimensions. The predominant interaction modality was hybrid, with communication content focusing on self-management education, emotional support, and related domains. Outcome measures evaluated health-related indicators and user adherence indicators. Conclusions: Chatbots hold considerable clinical application value in chronic disease management. However, current research has some limitations. Future research should further optimize interaction design, refine system functionalities, and fortify privacy protection measures to better facilitate the integration of chatbots into chronic disease management.
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.