ReviewIndustrial psychiatry journal
Systematic review of artificial intelligence enabled psychological interventions for depression and anxiety: A comprehensive analysis.
Review in Industrial psychiatry journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of chatbots as interventions for mental health conditions: scoping review of systematic reviews and meta-analyses.Frontiers in digital health · 2026Pooled it
- Mental health chatbots and their technical features: A systematic review of reviews and a thematic analysis.Global mental health (Cambridge, England) · 2026Review
- The Impact of Chatbots on Adolescent Mental Health Development: A Comprehensive Literature Review.Journal of multidisciplinary healthcare · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psychiatric disorders like depression and anxiety represent significant global health challenges, affecting individuals of all ages and contributing to a substantial disability burden worldwide. Despite advancements in mental health care, barriers such as cost, geographical limits, and social stigma may prevent individuals from receiving early psychological interventions. In recent years, artificial intelligence (AI) has emerged as a prominent tool to address these issues by facilitating early detection, personalized treatment, and intervention delivery for individuals experiencing depression and anxiety. Hence, the present systematic review focused on AI-enabled conversational chatbots for the identification and management of depression and anxiety. The current systematic review yielded a total of ten studies after a thorough analysis. The majority of studies were randomized controlled trials. The most frequently utilized AI method was conversational AI agents which are chatbots available through online software accessible via computers or smartphones. The investigations revealed significant outcomes by using AI for the enhancement of psychotherapy. The majority of studies showed a low risk (71.67%), indicating their reliability, while unclear studies (15%) exhibited some ambiguity without invalidating results. Conversely, studies classified as high risk (13.33%) indicated significant bias and potential errors. Chatbots have emerged as an effective medium for self-help depression and anxiety management. Studies have revealed significant positive outcomes, showing the potential of AI augmentation in psychotherapy to reduce clinical symptomatology. Notably, chatbot-delivered therapies have proven to be more successful than limited bibliotherapy, demonstrating their ability to effectively reduce the symptoms of depression and anxiety while encouraging a stronger therapeutic alliance among participants.
Indexed as
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.