Evidence map›Paper›PMID 40510413›Full record

ArticleFrontiers in digital health2025

Balancing risks and benefits: clinicians' perspectives on the use of generative AI chatbots in mental healthcare.

Lyndsey Hipgrave, Jessie Goldie, Simon Dennis, Amanda Coleman

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In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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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.

Lyndsey HipgraveMelbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
Jessie GoldieMelbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
Simon DennisMelbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
Amanda ColemanSchool of Psychological Sciences, Monash University, Clayton, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The use of generative-AI chatbots has proliferated in mental health, to support both clients and clinicians across a range of uses. This paper aimed to explore the perspectives of mental health clinicians regarding the risks and benefits of integrating generative-AI chatbots into the mental health landscape. Methods: Twenty-three clinicians participated in a 45-minute virtual interview, in which a series of open-ended and scale-based questions were asked, and a demonstration of a mental health chatbot's potential capabilities was presented. Results: Participants highlighted several benefits of chatbots, such as their ability to administer homework tasks, provide multilingual support, enhance accessibility and affordability of mental healthcare, offer access to up-to-date research, and increase engagement in some client groups. However, they also identified risks, including the lack of regulation, data and privacy concerns, chatbots' limited understanding of client backgrounds, potential for client over-reliance on chatbots, incorrect treatment recommendations, and the inability to detect subtle communication cues, such as tone and eye contact. There was no significant finding to suggest that participants viewed either the risks or benefits as outweighing the other. Moreover, a demonstration of potential chatbot capabilities was not found to influence whether participants favoured the risks or benefits of chatbots. Discussion: Qualitative responses revealed that the balance of risks and benefits is highly contextual, varying based on the use case and the population group being served. This study contributes important insights from critical stakeholders for chatbot developers to consider in future iterations of AI tools for mental health.

Indexed as

benefits riskschatbotsgenerative AImental healthmental health clinicians

Identifiers

PMID40510413
PMCPMC12158938

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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.