ArticleNPJ digital medicine2026
The effectiveness of CBT-based NLP-enabled AI conversational agents for mental health intervention: a systematic review and meta-analysis.
Article in NPJ digital medicine, 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
Abstract
Natural language processing (NLP)-enabled artificial intelligence (AI) conversational agents (CAs) are increasingly adopted in digital mental health interventions, yet the efficacy of such CAs grounded in cognitive behavioral therapy (CBT) remains unclear. This study aims to examine the intervention effectiveness of CBT-based NLP-enabled AI CAs in various mental health problems. A total of 15 randomized controlled trials with 1737 participants were included in the analysis. The results indicated that CBT-based NLP-enabled AI CAs showed a small to moderate effect on depressive symptoms and a small effect on negative affect; while the effects on generalized anxiety, stress, and positive affect were not significant after adjusting for publication bias. Subgroup analyses provided preliminary evidence that multi-modal CAs may be more effective than single-modality CAs in reducing depressive symptoms, and that the absence of psychoeducational content was associated with larger post-test effect sizes. Notably, meta-regression revealed that higher-quality studies reported larger effect sizes, suggesting that the true efficacy of these interventions may be underestimated in the current literature. In addition, younger age was associated with a greater reduction in depressive symptoms. These findings underscored the potential of CBT-based NLP-enabled AI CAs in addressing certain mental health issues and in certain populations.
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.