Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Previously submitted to: JMIR Mental Health (no longer under consideration since Jan 21, 2026)

Date Submitted: Jan 21, 2026

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

The effectiveness of AI chatbots in reducing anxiety and depression symptoms, and feelings of loneliness: A systematic review and meta-analyses

  • Laura Neil; 
  • Helen Haydon; 
  • Liam Caffery; 
  • Aaron Snoswell; 
  • Centaine Snoswell

ABSTRACT

Background:

Globally, there is a high prevalence of mental health conditions, but access to timely and appropriate care is limited by accessibility, stigma, wait times, and worker shortages. Artificial intelligence (AI) chatbots are an emerging technology offering an alternative to traditional forms of mental health support.

Objective:

The aim of this paper is to assess the effectiveness of AI chatbot interventions in reducing symptoms of anxiety and depression, and feelings of loneliness.

Methods:

PubMed, CINAHL and Embase databases were searched in April 2025 for studies published in peer-reviewed journals in English language since 2022. The search strategy included terms for mental health and AI. Eligible studies included a text-based synchronous AI chatbot as the main intervention for participant screening, diagnosis, or treating symptoms of anxiety or depression, or feelings of loneliness. Additionally, studies needed to: use a validated psychometric scale to measure effectiveness; include participants aged 16 years and over; and have a focus other than prenatal or perinatal mental health. Screening and selection were conducted by two of the authors. Eligibility disagreements were resolved by a third author. Data extraction was performed by one author and checked for data integrity by a second author. Meta-analyses of mean changes were performed for studies reporting results using the same psychometric scale, study method, and comparators. The primary outcomes were results of validated psychometric scales measuring symptoms of anxiety or depression, or feelings of loneliness. Results were compared at baseline and post-intervention. Study details and demographics were also reported.

Results:

Thirty studies met eligibility criteria and were included for review. Statistically significant improvement was found in 67% of studies measuring depression and 56% of studies measuring anxiety, but only 25% of studies measuring loneliness. There was a small mean reduction in symptoms for all conditions but these are not considered clinically significant.

Conclusions:

When measured using psychometric scales, AI chatbots may be effective in reducing anxiety and depression symptoms in some cases, and have the potential to be used in conjunction with other forms of mental health care for these conditions. They are less effective in reducing feelings of loneliness. According to present findings, chatbots are not consistently effective enough to be recommended as a stand-alone intervention for symptoms of anxiety and depression, or feelings of loneliness. Clinical Trial: PROSPERO ID: CRD420250651616


 Citation

Please cite as:

Neil L, Haydon H, Caffery L, Snoswell A, Snoswell C

The effectiveness of AI chatbots in reducing anxiety and depression symptoms, and feelings of loneliness: A systematic review and meta-analyses

JMIR Preprints. 21/01/2026:91864

DOI: 10.2196/preprints.91864

URL: https://preprints.jmir.org/preprint/91864

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.