ArticleNPJ digital medicine2026
Systematic review and meta analysis of chatbots in the management of depressive and anxiety symptoms.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Psychological Therapy in the Age of Large Language Models: Framework for Therapist-Delivered and AI-Supported Functions.JMIR mental health · 2026Article
- General-Purpose Artificial Intelligence Use in Routine Mental Health Practice Among Australian Clinicians: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Artificial intelligence in psychiatric care and education: a qualitative study of factors influencing adoption in Singapore.International journal of medical education · 2026Article
- Prediction of Treatment Benefit With Internet-Based Cognitive Behavioral Therapy for Depression.JAMA network open · 2026Article
- Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era.JMIR mental health · 2026Article
- Why sycophantic LLMs may imperil interactive norms between humans.Communications psychology · 2026Review
- Current themes of AI in mental health: Actionable evidence and guardrails for mood and anxiety care.Journal of mood and anxiety disorders · 2026Review
- Effectiveness of AI and rule-based conversational agents for depression, anxiety and stress: A meta-analysis.NPJ digital medicine · 2026Article
- Artificial intelligence as decision support for adolescent depression and anxiety: a mini review of clinical utility, safety, and implementation.Frontiers in psychiatry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Mental health chatbots have proliferated rapidly, yet their effectiveness remains unclear. This systematic review and meta-analysis included randomized controlled trials comparing chatbots with any control condition for depressive and/or anxiety outcomes. PubMed, Embase, PsycINFO, Scopus and Web of Science were searched from January 2017 to October 2025. Risk of bias was assessed using the revised Cochrane tool. Pooled effect sizes (Hedges' g) were calculated using random-effects models. Of the 39 eligible studies, 38 (n = 7,401) were analyzed for depression and 34 (n = 7,621) for anxiety. Chatbots produced statistically significant reductions in depressive (g = 0.31, 95% CI [0.17, 0.46]) and anxiety symptoms (g = 0.28, 95% CI [0.05, 0.51]) compared with controls. Subgroup analyses for depressive symptoms showed larger effects in clinical and subclinical than in nonclinical samples (p = 0.001). Contemporary chatbots thus appear to alleviate depressive and anxiety symptoms, especially in individuals with greater depressive severity. (PROSPERO registration: CRD42024598761).
Identifiers
What OpenQuestion 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 OpenQuestion graph.