ReviewDigital health
Natural language processing-based chatbots for chronic disease self-management: A systematic review of implementation and health outcomes.
Review in Digital health. 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
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.
- Generative AI as interactional infrastructure for meaning-centered care in later life.Frontiers in psychiatry · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Conversational agents (chatbots) are increasingly used as digital health interventions to support chronic disease self-management. Advances in natural language processing (NLP) have improved their capacity for interactive dialogue and personalization, yet evidence regarding their implementation and clinical impact remains limited. Objectives: This systematic review identifies and synthesizes studies implementing NLP-based chatbots for chronic disease self-management. Methods: We searched seven electronic databases (PubMed, Embase, CINAHL, Web of Science, Scopus, Cochrane Library, and IEEE Xplore) and Google Scholar for studies published between January 2010 and November 2025. Studies evaluating NLP-based chatbots designed to support chronic disease self-management were deemed eligible. Study quality and risk of bias were assessed using the Mixed Methods Appraisal Tool and the Quality Assessment with Diverse Studies instrument. Results: Six studies met the inclusion criteria; most were published in 2023 and targeted conditions such as cancer, diabetes, and hypertension. Chatbot functions primarily focused on symptom monitoring and disease-related education. Reported outcomes included improvements in disease-related knowledge, symptom burden, mental well-being, and self-care adherence. Usability and acceptability were generally favorable, with high satisfaction, perceived usefulness, and engagement. However, evidence of objective clinical benefits, including laboratory outcomes, was limited. Technical architectures varied widely, and advanced NLP capabilities-such as free-text natural language understanding-were rarely implemented. Conclusions: NLP-based chatbots show promise for supporting chronic disease self-management, particularly for psychosocial and behavioral outcomes. However, evidence of clinical efficacy remains limited. Future research should prioritize adaptive, context-aware designs and standardized outcome frameworks aligned with real-world self-management needs.
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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.