Evidence map›Paper›PMID 42215626›Full record

ArticleNPJ digital medicine2026

Efficacy of AI-delivered cognitive behavioral therapy interventions for anxiety and depressive symptoms: a systematic review.

Wing Lam Tiffany Yip, Ya Yambao Yang, Zilu Lucia Wang, David Stuckler

Abstract read
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In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wing Lam Tiffany YipNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. tiffany.1.yip@kcl.ac.uk.
Ya Yambao YangDepartment of Public Health, University of California, Davis, CA, USA.
Zilu Lucia WangDepartment of Education, University of Oxford, Oxford, UK. zilu.wang@education.ox.ac.uk.
David StucklerDepartment of Social and Political Sciences, University of Bocconi, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the availability of evidence-based treatments like CBT, many individuals still lack adequate care for anxiety and depression due to workforce shortages, systemic barriers, and cultural differences. AI-delivered CBT (AI CBT) offers a scalable solution to this issue, yet existing evidence remains fragmented and lacks diversity. This systematic review addresses this gap regarding AI CBTs. We searched Web of Science, PubMed, and PsycINFO for RCTs 2016 onward that evaluated AI CBT for anxiety or depressive symptoms. Of 208 studies screened, 16 met the inclusion criteria. Data was extracted and thematically synthesized. Study quality was assessed using the Cochrane Risk of Bias Tool 2. AI CBT showed limited efficacy for anxiety, with only 1 of 14 studies reporting significant improvements. Third-wave CBT and interventions targeting younger populations may show particular potential in reducing anxiety. For depressive symptoms, only 4 of 16 studies showed significant improvements. Across both conditions, highly preliminary evidence suggest that AI CBTs show less promise for older adults. Additionally, personalization and human support appeared to not be reliably associated with improved outcomes. Study quality also raised concerns. Overall, current AI CBTs require further refinement through high-quality and user-centered research. No funding went into this manuscript (PROSPERO: CRD42024615340).

Identifiers

What OpenQuestion holds

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Registered trials

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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.