ReviewCureus2026
The Role of Artificial Intelligence Chatbots in Chronic Disease Care: A Systematic Review of Current Evidence and Future Directions.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic diseases impose a substantial burden on healthcare systems, requiring innovative approaches to support long-term self-management. AI-driven chatbots have emerged as a scalable digital health intervention, but the evidence base remains fragmented. This systematic review synthesizes current evidence on the role of AI chatbots in chronic disease care and evaluates their impact on clinical and behavioural outcomes. A comprehensive literature search was conducted in PubMed, Embase, Scopus, and Web of Science for studies published between 2021 and 2025. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, and the narrative synthesis was reported in accordance with the Synthesis Without Meta-analysis (SWiM) guidelines; the certainty of the evidence was rated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Risk of bias was assessed using the Cochrane RoB 2 tool for RCTs and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for non-randomized studies (inter-reviewer agreement Cohen κ = 0.84-0.90). Eleven studies met the inclusion criteria, covering conditions such as diabetes, hypertension, asthma, cancer, mental health disorders, and smoking cessation. Chatbot technologies ranged from rule-based systems to advanced natural language processing (NLP) and large language models (LLMs). AI chatbots improved smoking cessation rates (26% vs. 18.8%; absolute difference 7.2 percentage points), reduced depression and anxiety in some studies, and demonstrated high usability and acceptability. No serious adverse events were reported. However, methodological limitations included small sample sizes, short follow-up periods, high attrition, and the absence of control groups, and the overall certainty of the evidence was low to very low across outcome domains. AI chatbots are feasible and acceptable for chronic disease care, particularly for smoking cessation and mental health support; however, current evidence is insufficient to confirm effects on hard clinical endpoints. Future research requires large-scale RCTs with objective clinical endpoints and longer follow-up.
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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.