Evidence map›Paper›PMID 41836860›Full record

ArticleProceedings (Baylor University. Medical Center)

Too good to be true? Exploring the role of artificial intelligence chatbots in treating depression and anxiety.

Jasleen Kaur, Muhammad Jamal Nasir, Luis Velez, Lakshit Jain, Mohsin Raza

Abstract read
In one paragraph

Article in Proceedings (Baylor University. Medical Center). 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

5 authors.

Jasleen KaurConnecticut Valley Hospital, Middletown, Connecticut, USA.ORCID https://orcid.org/0000-0003-4776-5927
Muhammad Jamal NasirFaisalabad Medical University, Faisalabad, Pakistan.
Luis VelezUniversity of Connecticut Health Center, Farmington, Connecticut, USA.
Lakshit JainUniversity of Connecticut Health Center, Farmington, Connecticut, USA.ORCID https://orcid.org/0000-0003-3080-9356
Mohsin RazaDover Behavioral Health System, Dover, Delaware, USA.ORCID https://orcid.org/0009-0008-5630-2936

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression and anxiety affect more than half a billion people worldwide, yet access to mental health care remains severely limited due to stigma, cost, geography, and workforce shortages. Internet-based cognitive behavioral therapy (iCBT) and, more recently, artificial intelligence (AI)-driven chatbots have emerged to bridge this gap. AI chatbots deliver 24/7 support through natural language processing and machine learning, simulating human-like conversations grounded in cognitive behavioral therapy principles. Evidence suggests that chatbot use improves engagement and reduces attrition compared with iCBT alone, with randomized trials and recent meta-analyses demonstrating short-term reductions in depressive and anxiety symptoms. Chatbots such as Woebot, Wysa, and Tess integrate mood tracking, automated check-ins, and structured therapeutic activities that enhance self-efficacy and emotional regulation. Despite these benefits, limitations remain, including the inability to replicate genuine empathy, risk of misinterpretation during crises, reliance on short-term evidence, and lack of standardized evaluation frameworks. Privacy and data security also represent significant ethical concerns. Future research should prioritize long-term, diverse studies, transparent reporting of therapeutic principles, and development of universal guidelines for safety and implementation. While AI chatbots cannot replace professional care, they represent an innovative and cost-effective complement to overburdened systems, particularly in resource-limited settings.

Indexed as

Anxietyartificial intelligencechatbotcognitive behavioral therapydepressiondigital mental health

Identifiers

PMID41836860
PMCPMC12981578

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.